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  • 1.
    Asa, Sylvia
    et al.
    Department of Pathology, University Health Network, Toronto, Ontario, Canada.
    Bodén, Anna
    Linköping University, Department of Clinical and Experimental Medicine. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Pantatnowitz, Liron
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, USA.
    2020 vision of digital pathology in action2019In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 10, no 27Article in journal (Refereed)
  • 2.
    Cervin, Ida
    et al.
    Sectra AB, Chalmers University of Technology, Sectra AB, Gothenburg, Sweden.
    Molin, Jesper
    Center for Medical Image Science and Visualization, Chalmers University of Technology, Sectra AB, Gothenburg, Sweden.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Improving the creation and reporting of structured findings during digital pathology review2016In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 7, no 1, p. 32-32Article in journal (Refereed)
    Abstract [en]

    Background: Today, pathology reporting consists of many separate tasks, carried out by multiple people. Common tasks include dictation during case review, transcription, verification of the transcription, report distribution, and report the key findings to follow-up registries. Introduction of digital workstations makes it possible to remove some of these tasks and simplify others. This study describes the work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Methods: We explored the possibility to have a digital tool that simplifies image review by assisting note-taking, and with minimal extra effort, populates a structured report. Thus, our prototype sees reporting as an activity interleaved with image review rather than a separate final step. We created an interface to collect, sort, and display findings for the most common reporting needs, such as tumor size, grading, and scoring. Results: The interface was designed to reduce the need to retain partial findings in the head or on paper, while at the same time be structured enough to support automatic extraction of key findings for follow-up registry reporting. The final prototype was evaluated with two pathologists, diagnosing complicated partial mastectomy cases. The pathologists experienced that the prototype aided them during the review and that it created a better overall workflow. Conclusions: These results show that it is feasible to simplify the reporting tasks in a way that is not distracting, while at the same time being able to automatically extract the key findings. This simplification is possible due to the realization that the structured format needed for automatic extraction of data can be used to offload the pathologists' working memory during the diagnostic review.

  • 3.
    Chow, Joyce A
    et al.
    RISE Interactive Institute, Norrköping, Sweden.
    Törnros, Martin E
    Interaktiva Rum Sverige, Gothenburg, Sweden.
    Waltersson, Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Richard, Helen
    Region Östergötland, Center for Diagnostics, Clinical pathology.
    Kusoffsky, Madeleine
    RISE Interactive Institute, Norrköping, Sweden.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Sectra AB, Linköping, Sweden.
    Kurti, Arianit
    RISE Interactive Institute, Norrköping, Sweden.
    A Design Study Investigating Augmented Reality and Photograph Annotation in a Digitalized Grossing Workstation2017In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 8, no 31Article in journal (Refereed)
    Abstract [en]

    Context: Within digital pathology, digitalization of the grossing procedure has been relatively underexplored in comparison to digitalization of pathology slides. 

    Aims: Our investigation focuses on the interaction design of an augmented reality gross pathology workstation and refining the interface so that information and visualizations are easily recorded and displayed in a thoughtful view. 

    Settings and Design: The work in this project occurred in two phases: the first phase focused on implementation of an augmented reality grossing workstation prototype while the second phase focused on the implementation of an incremental prototype in parallel with a deeper design study. 

    Subjects and Methods: Our research institute focused on an experimental and “designerly” approach to create a digital gross pathology prototype as opposed to focusing on developing a system for immediate clinical deployment. 

    Statistical Analysis Used: Evaluation has not been limited to user tests and interviews, but rather key insights were uncovered through design methods such as “rapid ethnography” and “conversation with materials”. 

    Results: We developed an augmented reality enhanced digital grossing station prototype to assist pathology technicians in capturing data during examination. The prototype uses a magnetically tracked scalpel to annotate planned cuts and dimensions onto photographs taken of the work surface. This article focuses on the use of qualitative design methods to evaluate and refine the prototype. Our aims were to build on the strengths of the prototype's technology, improve the ergonomics of the digital/physical workstation by considering numerous alternative design directions, and to consider the effects of digitalization on personnel and the pathology diagnostics information flow from a wider perspective. A proposed interface design allows the pathology technician to place images in relation to its orientation, annotate directly on the image, and create linked information. 

    Conclusions: The augmented reality magnetically tracked scalpel reduces tool switching though limitations in today's augmented reality technology fall short of creating an ideal immersive workflow by requiring the use of a monitor. While this technology catches up, we recommend focusing efforts on enabling the easy creation of layered, complex reports, linking, and viewing information across systems. Reflecting upon our results, we argue for digitalization to focus not only on how to record increasing amounts of data but also how these data can be accessed in a more thoughtful way that draws upon the expertise and creativity of pathology professionals using the systems.

  • 4. Ernvik, Aron
    et al.
    Bergström, Staffan
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology.
    Ynnerman, Anders
    Linköping University.
    Image data set compression based on viewing parameters for storing medical image data from multidimensional data sets, related systems, methods and computer products2012Patent (Other (popular science, discussion, etc.))
  • 5.
    Falk, Martin
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Hotz, Ingrid
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Leeds Teaching Hospitals NHS Trust, United Kingdom.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB.
    Transfer Function Design Toolbox for Full-Color Volume Datasets2017In: 2017 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS), IEEE, IEEE, 2017, p. 171-179Conference paper (Refereed)
    Abstract [en]

    In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

  • 6.
    Falk, Martin
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Leeds Teaching Hospitals NHS Trust, United Kingdom.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    Interactive Visualization of 3D Histopathology in Native Resolution2019In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 1, p. 1008-1017Article in journal (Refereed)
    Abstract [en]

    We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 x 100,000 x 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.

  • 7.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden .
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Sectra, Linköping, Sweden .
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis2014In: Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan / [ed] Jianhua Yao,Tobias Klinder, Shuo Li, Springer, 2014, Vol. 17, p. 123-134Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose the concept of eigenspine, a data analysis schemeuseful for quantifying the linear correlation between different measures relevant fordescribing spinal deformities associated with spinal diseases, such as idiopathic scoliosis.The proposed concept builds upon the use of principal component analysis(PCA) and canonical correlation analysis (CCA), where PCA is used to reduce thenumber of dimensions in the measurement space, thereby providing a regularizationof the measurements, and where CCA is used to determine the linear dependence betweenpair-wise combinations of the different measures. To demonstrate the usefulnessof the eigenspine concept, the measures describing position and rotation of thelumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosiswere analyzed. The analysis showed that the strongest linear relationship is foundbetween the anterior-posterior displacement and the sagittal rotation of the vertebrae,and that a somewhat weaker but still strong correlation is found between thelateral displacement and the frontal rotation of the vertebrae. These results are wellin-line with the general understanding of idiopathic scoliosis. Noteworthy though isthat the obtained results from the analysis further proposes axial vertebral rotationas a differentiating measure when characterizing idiopathic scoliosis. Apart fromanalyzing pair-wise linear correlations between different measures, the method isbelieved to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

  • 8.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Model-based registration for assessment of spinal deformities in idiopathic scoliosis2014In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 2, p. 311-326Article in journal (Refereed)
    Abstract [en]

    Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

  • 9.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes2011In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, p. 592-603Conference paper (Refereed)
    Abstract [en]

    The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

  • 10.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Vavruch, Ludvig
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Neurosurgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Tropp, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Orthopaedics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis2013In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, no 6, p. 1775-1787Article in journal (Refereed)
    Abstract [en]

    Reliable measurements of spinal deformities in idiopathic scoliosis are vital, since they are used for assessing the degree of scoliosis, deciding upon treatment and monitoring the progression of the disease. However, commonly used two dimensional methods (e.g. the Cobb angle) do not fully capture the three dimensional deformity at hand in scoliosis, of which axial vertebral rotation (AVR) is considered to be of great importance. There are manual methods for measuring the AVR, but they are often time-consuming and related with a high intra- and inter-observer variability. In this paper, we present a fully automatic method for estimating the AVR in images from computed tomography. The proposed method is evaluated on four scoliotic patients with 17 vertebrae each and compared with manual measurements performed by three observers using the standard method by Aaro-Dahlborn. The comparison shows that the difference in measured AVR between automatic and manual measurements are on the same level as the inter-observer difference. This is further supported by a high intraclass correlation coefficient (0.971-0.979), obtained when comparing the automatic measurements with the manual measurements of each observer. Hence, the provided results and the computational performance, only requiring approximately 10 to 15 s for processing an entire volume, demonstrate the potential clinical value of the proposed method.

  • 11.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Sectra, Linköping, Sweden.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eigenspine: Computing the Correlation between Measures Describing Vertebral Pose for Patients with Adolescent Idiopathic Scoliosis2014In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 38, no 7, p. 549-557Article in journal (Refereed)
    Abstract [en]

    This paper describes the concept of eigenspine, a concept applicable for determining the correlation between pair-wise combinationsof measures useful for describing the three-dimensional spinal deformities associated with adolescent idiopathic scoliosis. Theproposed data analysis scheme is based upon the use of principal component analysis (PCA) and canonical correlation analysis(CCA). PCA is employed to reduce the dimensionality of the data space, thereby providing a regularization of the measurements,and CCA is employed to determine the linear dependence between pair-wise combinations of different measures. The usefulness ofthe eigenspine concept is demonstrated by analyzing the position and the rotation of all lumbar and thoracic vertebrae as obtainedfrom 46 patients suffering from adolescent idiopathic scoliosis. The analysis showed that the strongest linear relationship is foundbetween the lateral displacement and the coronal rotation of the vertebrae, and that a somewhat weaker but still strong correlationis found between the coronal rotation and the axial rotation of the vertebrae. These results are well in-line with the generalunderstanding of idiopathic scoliosis. Noteworthy though is that the correlation between the anterior-posterior displacement and thesagittal rotation was not as strong as expected and that the obtained results further indicate the need for including the axial vertebralrotation as a measure when characterizing different types of idiopathic scoliosis. Apart from analyzing pair-wise correlationsbetween different measures, the method is believed to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

  • 12.
    Hoefener, Henning
    et al.
    Fraunhofer MEVIS, Germany.
    Homeyer, Andre
    Fraunhofer MEVIS, Germany.
    Weiss, Nick
    Fraunhofer MEVIS, Germany.
    Molin, Jesper
    Sectra AB, Teknikringen 20, S-58330 Linkoping, Sweden.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Teknikringen 20, S-58330 Linkoping, Sweden.
    Hahn, Horst K.
    Fraunhofer MEVIS, Germany; Jacobs Univ, Germany.
    Deep learning nuclei detection: A simple approach can deliver state-of-the-art results2018In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 70, p. 43-52Article in journal (Refereed)
    Abstract [en]

    Background: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of a nucleus center, which we refer to as PMap. Nevertheless, these implementations often still differ in several parameters, resulting in different detection qualities. Methods: We identified several essential parameters and configured the basic PMap approach using combinations of them. We thoroughly evaluated and compared various configurations on multiple datasets with respect to detection quality, efficiency and training effort. Results: Post-processing of the PMap was found to have the largest impact on detection quality. Also, two different network architectures were identified that improve either detection quality or runtime performance. The best-performing configuration yields f1-measures of 0.816 on Hamp;E stained images of colorectal adenocarcinomas and 0.819 on Ki-67 stained images of breast tumor tissue. On average, it was fully trained in less than 15,000 iterations and processed 4.15 megapixels per second at prediction time. Conclusions: The basic PMap approach is greatly affected by certain parameters. Our evaluation provides guidance on their impact and best settings. When configured properly, this simple and efficient approach can yield equal detection quality as more complex and time-consuming state-of-the-art approaches. (C) 2018 The Authors. Published by Elsevier Ltd.

  • 13.
    Homeyer, Andre
    et al.
    Fraunhofer MEVIS, Germany.
    Nasr, Patrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Engel, Christiane
    Fraunhofer MEVIS, Germany.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ekstedt, Mattias
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Kost, Henning
    Fraunhofer MEVIS, Germany.
    Weiss, Nick
    Fraunhofer MEVIS, Germany.
    Palmer, Tim
    University of Leeds, England.
    Karl Hahn, Horst
    Fraunhofer MEVIS, Germany.
    Treanor, Darren
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. University of Leeds, England; Leeds Teaching Hospital NHS Trust, England.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Automated quantification of steatosis: agreement with stereological point counting2017In: Diagnostic Pathology, ISSN 1746-1596, E-ISSN 1746-1596, Vol. 12, article id 80Article in journal (Refereed)
    Abstract [en]

    Background: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. Methods: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. Results: The new method showed the strongest agreement with the expert. At 20x resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10x resolution, it was more accurate than and twice as fast as all other methods at 20x resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. Conclusions: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.

  • 14.
    Kost, Henning
    et al.
    Fraunhofer Mevis.
    Homeyer, André
    Fraunhofer Mevis.
    Molin, Jesper
    Sectra AB.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Hahn, Horst
    Fraunhofer Mevis.
    Training Nuclei Detection Algorithms with Simple Annotations2017In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 8Article in journal (Refereed)
    Abstract [en]

    Background:

    Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible.

    Methods:

    We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images.

    Results:

    A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality.

    Conclusions:

    With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

  • 15.
    Källén, Hanna
    et al.
    Centre for Mathematical Sciences, Lund University, Sweden.
    Molin, Jesper
    Linköping University, Center for Medical Image Science and Visualization (CMIV). t2iLab, Chalmers University of Technology, Sweden; Sectra AB, Linköping, Sweden.
    Heyden, Anders
    Centre for Mathematical Sciences, Lund University, Sweden.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden.
    Åström, Kalle
    Centre for Mathematical Sciences, Lund University, Sweden.
    Towards grading gleason score using generically trained deep convolutional neural networks2016In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1163-1167Conference paper (Refereed)
    Abstract [en]

    We developed an automatic algorithm with the purpose to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen. In order to detect and classify the cancerous tissue, a deep convolutional neural network that had been pre-trained on a large set of photographic images was used. A specific aim was to support intuitive interaction with the result, to let pathologists adjust and correct the output. Therefore, we have designed an algorithm that makes a spatial classification of the whole slide into the same growth patterns as pathologists do. The 22-layer network was cut at an earlier layer and the output from that layer was used to train both a random forest classifier and a support vector machines classifier. At a specific layer a small patch of the image was used to calculate a feature vector and an image is represented by a number of those vectors. We have classified both the individual patches and the entire images. The classification results were compared for different scales of the images and feature vectors from two different layers from the network. Testing was made on a dataset consisting of 213 images, all containing a single class, benign tissue or Gleason score 35. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.

  • 16.
    Lindholm, Stefan
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Forsberg, Daniel
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Towards Clinical Deployment of Automated Anatomical Regions-Of-Interest2014In: Eurographics Workshop on Visual Computing for Biology and Medicine / [ed] Ivan Viola and Katja Buehler and Timo Ropinski, Eurographics - European Association for Computer Graphics, 2014, p. 137-143Conference paper (Refereed)
    Abstract [en]

    The purpose of this work is to investigate, and improve, the feasibility of advanced Region Of Interest (ROI) selection schemes in clinical volume rendering. In particular, this work implements and evaluates an Automated Anatomical ROI (AA-ROI) approach based on the combination of automatic image registration (AIR) and Distance-Based Transfer Functions (DBTFs), designed for automatic selection of complex anatomical shapes without relying on prohibitive amounts of interaction. Domain knowledge and clinical experience has been included in the project through participation of practicing radiologists in all phases of the project. This has resulted in a set of requirements that are critical for Direct Volume Rendering applications to be utilized in clinical practice and a prototype AA-ROI implementation that was developed to addresses critical points in existing solutions. The feasibility of the developed approach was assessed through a study where five radiologists investigated three medical data sets with complex ROIs, using both traditional tools and the developed prototype software. Our analysis indicate that advanced, registration based ROI schemes could increase clinical efficiency in time-critical settings for cases with complex ROIs, but also that their clinical feasibility is conditional with respect to the radiologists trust in the registration process and its application to the data.

  • 17.
    Lindholm, Stefan
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ljung, Patric
    Siemens Corporate Research, USA .
    Lundström, Claes
    Sectra Imtec AB, Sweden .
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Spatial Conditioning of Transfer Functions Using Local Material Distributions2010In: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, ISSN 1077-2626, Vol. 16, no 6, p. 1301-1310Article in journal (Refereed)
    Abstract [en]

    In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patients symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the users specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

  • 18.
    Lindman, Karin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Rose, Jeronimo
    Lindvall, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Sectra AB.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine.
    Annotations, ontologies, and whole slide images–Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue2019In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 10, no 22Article in journal (Refereed)
  • 19.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Multiresolution Interblock Interpolation in Direct Volume Rendering2006In: Proceedings of Eurographics/IEEE Symposium on Visualization 2006, Lisbon, Portugal, 2006, p. 259-266Conference paper (Other academic)
    Abstract [en]

    We present a direct interblock interpolation technique that enables direct volume rendering of blocked, multiresolution volumes. The proposed method smoothly interpolates between blocks of arbitrary block-wise level-of-detail (LOD) without sample replication or padding. This permits extreme changes in resolution across block boundaries and removes the interblock dependency for the LOD creation process. In addition the full data reduction from the LOD selection can be maintained throughout the rendering pipeline. Our rendering pipeline employs a flat block subdivision followed by a transfer function based adaptive LOD scheme. We demonstrate the effectiveness of our method by rendering volumes of the order of gigabytes using consumer graphics cards on desktop PC systems.

  • 20.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Transfer Function Based Adaptive Decompresion for Volume Rendering of Large Medical Data Sets2004In: Proceedings of IEEE/ACM Symposium on Volume Visualization 2004, Austin, USA, IEEE , 2004, p. 25-32Conference paper (Refereed)
    Abstract [en]

    The size of standard volumetric data sets in medical imaging is rapidly increasing causing severe performance limitations in direct volume rendering pipelines. The methods presented in this paper exploit the medical knowledge embedded in the transfer function to reduce the required bandwidth in the pipeline. Typically, medical transfer functions cause large subsets of the volume to give little or no contribution to the rendered image. Thus, parts of the volume can be represented at low resolution while retaining overall visual quality. This paper introduces the use of transfer functions at decompression time to guide a level-of-detail selection scheme. The method may be used in combination with traditional lossy or lossless compression schemes. We base our current implementation on a multi-resolution data representation using compressed wavelet transformed blocks. The presented results using the adaptive decompression demonstrate a significant reduction in the required amount of data while maintaining rendering quality. Even though the focus of this paper is medical imaging, the results are applicable to volume rendering in many other domains.

  • 21.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Winskog, Calle
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Full Body Virtual Autopsies using a State-of-the-art Volume Rendering Pipeline2006In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, Vol. 12, no 5, p. 869-876Article in journal (Refereed)
    Abstract [en]

    This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolution data from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added value and future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands of the procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsies involving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof- detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large, out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of the pipeline are presented using data sets from forensic cases.

  • 22.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Winskog, Calle
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Full Body Virtual Autopsies Using A State-of-the-art Volume Rendering Pipeline2006In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 12, no 5, p. 869-876Article in journal (Other academic)
    Abstract [en]

    This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolutiondata from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added valueand future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands ofthe procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsiesinvolving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large,out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of thepipeline are presented using data sets from forensic cases.

  • 23.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Winskog, Carl
    Pathology Section, The Forensic Sciences Centre, Barbados.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology UHL.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra-Imtec AB, Linköping, Sweden.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forensic Virtual Autopsies by Direct Volume Rendering2007In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 24, no 6, p. 112-116Article in journal (Other academic)
    Abstract [en]

    This paper presents state-of-the-art methods, which address the technical challenges in visualizing large three-dimensional (3D) data and enable rendering at interactive frame rates.

  • 24.
    Lundin (Palmerius), Karljohan
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Enabling Haptic Interaction with Volumetric MRI Data Through Knowledge-based Tissue Separation2006In: Proceedings of Volume Graphics, 2006, p. 75-78Conference paper (Refereed)
    Abstract [en]

    Direct volume haptics can provide both guidance and extra information during exploration of volumetric data. In this paper we present a novel approach to volume haptics enabling haptic exploration of tissue shape, borders and material properties in data despite low contrast and low signal to noise ratio, as is common in medical MRI data or low dose CT data. The method uses filtering based on implicit knowledge and addresses the problem of overlapping scalar ranges through the introduction of fuzzy classification and corresponding transfer functions for material properties as well as classification-based distance masking for haptic force direction.

  • 25.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge2007Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Direct Volume Rendering (DVR) is a visualization technique that has proved to be a very powerful tool in many scientific visualization applications. Diagnostic medical imaging is one domain where DVR could provide clear benefits in terms of unprecedented possibilities for analysis of complex cases and highly efficient work flow for certain routine examinations. The full potential of DVR in the clinical environment has not been reached, however, primarily due to limitations in conventional DVR methods and tools.

    This thesis presents methods addressing four major challenges for DVR in clinical use. The foundation of all methods is to incorporate the domain knowledge of the medical professional in the technical solutions. The first challenge is the very large data sets routinely produced in medical imaging today. To this end a multiresolution DVR pipeline is proposed, which dynamically prioritizes data according to the actual impact in the rendered image to be reviewed. Using this prioritization the system can reduce the data requirements throughout the pipeline and provide high performance and visual quality in any environment.

    Another problem addressed is how to achieve simple yet powerful interactive tissue classification in DVR. The methods presented define additional attributes that effectively captures readily available medical knowledge. The task of tissue detection is also important to solve in order to improve efficiency and consistency of diagnostic image review. Histogram-based techniques that exploit spatial relations in the data to achieve accurate and robust tissue detection are presented in this thesis.

    The final challenge is uncertainty visualization, which is very pertinent in clinical work for patient safety reasons. An animation method has been developed that automatically conveys feasible alternative renderings. The basis of this method is a probabilistic interpretation of the visualization parameters.

    Several clinically relevant evaluations of the developed techniques have been performed demonstrating their usefulness. Although there is a clear focus on DVR and medical imaging, most of the methods provide similar benefits also for other visualization techniques and application domains.

    List of papers
    1. Transfer Function Based Adaptive Decompresion for Volume Rendering of Large Medical Data Sets
    Open this publication in new window or tab >>Transfer Function Based Adaptive Decompresion for Volume Rendering of Large Medical Data Sets
    2004 (English)In: Proceedings of IEEE/ACM Symposium on Volume Visualization 2004, Austin, USA, IEEE , 2004, p. 25-32Conference paper, Published paper (Refereed)
    Abstract [en]

    The size of standard volumetric data sets in medical imaging is rapidly increasing causing severe performance limitations in direct volume rendering pipelines. The methods presented in this paper exploit the medical knowledge embedded in the transfer function to reduce the required bandwidth in the pipeline. Typically, medical transfer functions cause large subsets of the volume to give little or no contribution to the rendered image. Thus, parts of the volume can be represented at low resolution while retaining overall visual quality. This paper introduces the use of transfer functions at decompression time to guide a level-of-detail selection scheme. The method may be used in combination with traditional lossy or lossless compression schemes. We base our current implementation on a multi-resolution data representation using compressed wavelet transformed blocks. The presented results using the adaptive decompression demonstrate a significant reduction in the required amount of data while maintaining rendering quality. Even though the focus of this paper is medical imaging, the results are applicable to volume rendering in many other domains.

    Place, publisher, year, edition, pages
    IEEE, 2004
    Keywords
    Adaptive decompression, Image quality measures, Medical imaging, Multiresolution, Transfer function, Volume compression, Volume rendering, Wavelet transform
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13943 (URN)10.1109/SVVG.2004.14 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
    2. Extending and Simplifying Transfer Function Design in Medical Volume Rendering Using Local Histograms
    Open this publication in new window or tab >>Extending and Simplifying Transfer Function Design in Medical Volume Rendering Using Local Histograms
    2005 (English)In: Proceedings EuroGraphics/IEEE Symposium on Visualization 2005, Leeds, UK, 2005, p. 263-270Conference paper, Published paper (Other academic)
    Abstract [en]

    Direct Volume Rendering (DVR) is known to be of diagnostic value in the analysis of medical data sets. However, its deployment in everyday clinical use has so far been limited. Two major challenges are that the current methods for Transfer Function (TF) construction are too complex and that the tissue separation abilities of the TF need to be extended. In this paper we propose the use of histogram analysis in local neighborhoods to address both these conflicting problems. To reduce TF construction difficulty, we introduce Partial Range Histograms in an automatic tissue detection scheme, which in connection with Adaptive Trapezoids enable efficient TF design. To separate tissues with overlapping intensity ranges, we propose a fuzzy classification based on local histograms as a second TF dimension. This increases the power of the TF, while retaining intuitive presentation and interaction.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13944 (URN)10.2312/VisSym/EuroVis05/263-270 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
    3. Standardized volume rendering for magnetic resonance angiography measurements in the abdominal aorta
    Open this publication in new window or tab >>Standardized volume rendering for magnetic resonance angiography measurements in the abdominal aorta
    Show others...
    2006 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 47, no 2, p. 172-178Article in journal (Refereed) Published
    Abstract [en]

    Purpose: To compare three methods for standardizing volume rendering technique (VRT) protocols by studying aortic diameter measurements in magnetic resonance angiography (MRA) datasets.

    Material and Methods: Datasets from 20 patients previously examined with gadolinium-enhanced MRA and with digital subtraction angiography (DSA) for abdominal aortic aneurysm were retrospectively evaluated by three independent readers. The MRA datasets were viewed using VRT with three different standardized transfer functions: the percentile method (Pc-VRT), the maximum-likelihood method (ML-VRT), and the partial range histogram method (PRH-VRT). The aortic diameters obtained with these three methods were compared with freely chosen VRT parameters (F-VRT) and with maximum intensity projection (MIP) concerning inter-reader variability and agreement with the reference method DSA.

    Results: F-VRT parameters and PRH-VRT gave significantly higher diameter values than DSA, whereas Pc-VRT gave significantly lower values than DSA. The highest interobserver variability was found for F-VRT parameters and MIP, and the lowest for Pc-VRT and PRH-VRT. All standardized VRT methods were significantly superior to both MIP and F-VRT in this respect. The agreement with DSA was best for PRH-VRT, which was the only method with a mean error below 1 mm and which also had the narrowest limits of agreement (95% of cases between 2.1 mm below and 3.1 mm above DSA).

    Conclusion: All the standardized VRT methods compare favorably with MIP and VRT with freely selected parameters as regards interobserver variability. The partial range histogram method, although systematically overestimating vessel diameters, gives results closest to those of DSA.

    Keywords
    Abdominal aortic aneurysm (AAA); angiography; magnetic resonance angiography (MRA); maximum intensity projection (MIP); volume rendering technique (VRT); user dependence
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-14591 (URN)10.1080/02841850500445298 (DOI)000236669500010 ()
    Available from: 2007-08-24 Created: 2007-08-24 Last updated: 2017-12-13Bibliographically approved
    4. Multiresolution Interblock Interpolation in Direct Volume Rendering
    Open this publication in new window or tab >>Multiresolution Interblock Interpolation in Direct Volume Rendering
    2006 (English)In: Proceedings of Eurographics/IEEE Symposium on Visualization 2006, Lisbon, Portugal, 2006, p. 259-266Conference paper, Published paper (Other academic)
    Abstract [en]

    We present a direct interblock interpolation technique that enables direct volume rendering of blocked, multiresolution volumes. The proposed method smoothly interpolates between blocks of arbitrary block-wise level-of-detail (LOD) without sample replication or padding. This permits extreme changes in resolution across block boundaries and removes the interblock dependency for the LOD creation process. In addition the full data reduction from the LOD selection can be maintained throughout the rendering pipeline. Our rendering pipeline employs a flat block subdivision followed by a transfer function based adaptive LOD scheme. We demonstrate the effectiveness of our method by rendering volumes of the order of gigabytes using consumer graphics cards on desktop PC systems.

    Keywords
    Viewing algorithms; Image Processing; Computer Vision
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13945 (URN)10.2312/VisSym/EuroVis06/259-266 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
    5. The alpha-histogram: Using Spatial Coherence to Enhance Histograms and Transfer Function Design
    Open this publication in new window or tab >>The alpha-histogram: Using Spatial Coherence to Enhance Histograms and Transfer Function Design
    Show others...
    2006 (English)In: Proceedings Eurographics/IEEE Symposium on Visualization 2006, Lisbon, Portugal, 2006, p. 227-234Conference paper, Published paper (Other academic)
    Abstract [en]

    The high complexity of Transfer Function (TF) design is a major obstacle to widespread routine use of Direct Volume Rendering, particularly in the case of medical imaging. Both manual and automatic TF design schemes would benefit greatly from a fast and simple method for detection of tissue value ranges. To this end, we introduce the a-histogram, an enhancement that amplifies ranges corresponding to spatially coherent materials. The properties of the a-histogram have been explored for synthetic data sets and then successfully used to detect vessels in 20 Magnetic Resonance angiographies, proving the potential of this approach as a fast and simple technique for histogram enhancement in general and for TF construction in particular.

    Keywords
    Picture/Image Generation; Methodology and Techniques; Three-Dimensional Graphics and Realism
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13946 (URN)10.2312/VisSym/EuroVis06/227-234 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
    6. Multi-Dimensional Transfer Function Design Using Sorted Histograms
    Open this publication in new window or tab >>Multi-Dimensional Transfer Function Design Using Sorted Histograms
    2006 (English)In: Proceedings Eurographics/IEEE International Workshop on Volume Graphics 2006, Boston, USA, 2006, p. 1-8Conference paper, Published paper (Other academic)
    Abstract [en]

    Multi-dimensional Transfer Functions (MDTFs) are increasingly used in volume rendering to produce high quality visualizations of complex data sets. A major factor limiting the use of MDTFs is that the available design tools have not been simple enough to reach wide usage outside of the research context, for instance in clinical medical imaging. In this paper we address this problem by defining an MDTF design concept based on improved histogram display and interaction in an exploratory process. To this end we propose sorted histograms, 2D histograms that retain the intuitive appearance of a traditional 1D histogram while conveying a second attribute. We deploy the histograms in medical visualizations using data attributes capturing domain knowledge e.g. in terms of homogeneity and typical surrounding of tissues. The resulting renderings demonstrate that the proposed concept supports a vast number of visualization possibilities based on multi-dimensional attribute data.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13948 (URN)10.2312/VG/VG06/001-008 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
    7. Local histograms for design of Transfer Functions in Direct Volume Rendering
    Open this publication in new window or tab >>Local histograms for design of Transfer Functions in Direct Volume Rendering
    2006 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 12, no 6, p. 1570-1579Article in journal (Other academic) Published
    Abstract [en]

    Direct Volume Rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current Transfer Function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce Partial Range Histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.

    Keywords
    Volume visualization, transfer function, medical imaging, classification, partial range histogram
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13949 (URN)10.1109/TVCG.2006.100 (DOI)
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2017-12-13
    8. Full Body Virtual Autopsies Using A State-of-the-art Volume Rendering Pipeline
    Open this publication in new window or tab >>Full Body Virtual Autopsies Using A State-of-the-art Volume Rendering Pipeline
    Show others...
    2006 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 12, no 5, p. 869-876Article in journal (Other academic) Published
    Abstract [en]

    This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolutiondata from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added valueand future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands ofthe procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsiesinvolving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large,out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of thepipeline are presented using data sets from forensic cases.

    Keywords
    Forensics, autopsies, medical visualization, volume rendering, large scale data
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13950 (URN)10.1109/TVCG.2006.146 (DOI)000241383300028 ()
    Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2017-12-13
    9. Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
    Open this publication in new window or tab >>Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
    2007 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 13, no 6, p. 1648-1655Article in journal (Refereed) Published
    Abstract [en]

    Direct volume rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

    Keywords
    uncertainty, medical visualization, probability, transfer function, volume rendering
    National Category
    Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-14597 (URN)10.1109/TVCG.2007.70518 (DOI)000250401100076 ()
    Available from: 2007-08-24 Created: 2007-08-24 Last updated: 2017-12-13
  • 26.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Technical report: Measuring digital image quality2006Report (Other academic)
    Abstract [en]

    Imaging is an invaluable tool in many research areas and other advanced domains such as health care. When developing any system dealing with images, image quality issues are insurmountable. This report describes digital image quality from many viewpoints, from retinal receptor characteristics to perceptual compression algorithms. Special focus is given to perceptual image quality measures.

  • 27.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging2013In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 8, no 3, p. 437-450Article in journal (Refereed)
    Abstract [en]

    Purpose Often, the large amounts of data generated in diagnosticimaging cause overload problems for IT systems andradiologists. This entails a need of effective use of data reductionbeyond lossless levels, which, in turn, underlines theneed to measure and control the image fidelity. Existingimage fidelity metrics, however, fail to fully support importantrequirements from a modern clinical context: supportfor high-dimensional data, visualization awareness, and independencefrom the original data.Methods We propose an image fidelity metric, called thevisual peak signal-to-noise ratio (vPSNR), fulfilling the threemain requirements. A series of image fidelity tests on CTdata sets is employed. The impact of visualization transform(grayscalewindow) on diagnostic quality of irreversiblycompressed data sets is evaluated through an observer-basedstudy. In addition, several tests were performed demonstratingthe benefits, limitations, and characteristics of vPSNR indifferent data reduction scenarios.Results The visualization transform has a significant impacton diagnostic quality, and the vPSNR is capable of representingthis effect. Moreover, the tests establish that the vPSNRis broadly applicable.Conclusions vPSNR fills a gap not served by existingimage fidelity metrics, relevant for the clinical context. WhilevPSNR alone cannot fulfill all image fidelity needs, it can bea useful complement in a wide range of scenarios.

  • 28.
    Lundström, Claes F
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    Gilmore, Hannah L.
    Case Western Reserve University, OH 44106 USA.
    Ros, Pablo R.
    Case Western Reserve University, OH 44106 USA.
    Integrated Diagnostics: The Computational Revolution Catalyzing Cross-disciplinary Practices in Radiology, Pathology, and Genomics2017In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 285, no 1, p. 12-15Article in journal (Other academic)
    Abstract [en]

    n/a

  • 29.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Extending and Simplifying Transfer Function Design in Medical Volume Rendering Using Local Histograms2005In: Proceedings EuroGraphics/IEEE Symposium on Visualization 2005, Leeds, UK, 2005, p. 263-270Conference paper (Other academic)
    Abstract [en]

    Direct Volume Rendering (DVR) is known to be of diagnostic value in the analysis of medical data sets. However, its deployment in everyday clinical use has so far been limited. Two major challenges are that the current methods for Transfer Function (TF) construction are too complex and that the tissue separation abilities of the TF need to be extended. In this paper we propose the use of histogram analysis in local neighborhoods to address both these conflicting problems. To reduce TF construction difficulty, we introduce Partial Range Histograms in an automatic tissue detection scheme, which in connection with Adaptive Trapezoids enable efficient TF design. To separate tissues with overlapping intensity ranges, we propose a fuzzy classification based on local histograms as a second TF dimension. This increases the power of the TF, while retaining intuitive presentation and interaction.

  • 30.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Local histograms for design of Transfer Functions in Direct Volume Rendering2006In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 12, no 6, p. 1570-1579Article in journal (Other academic)
    Abstract [en]

    Direct Volume Rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current Transfer Function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce Partial Range Histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.

  • 31.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Multi-Dimensional Transfer Function Design Using Sorted Histograms2006In: Proceedings Eurographics/IEEE International Workshop on Volume Graphics 2006, Boston, USA, 2006, p. 1-8Conference paper (Other academic)
    Abstract [en]

    Multi-dimensional Transfer Functions (MDTFs) are increasingly used in volume rendering to produce high quality visualizations of complex data sets. A major factor limiting the use of MDTFs is that the available design tools have not been simple enough to reach wide usage outside of the research context, for instance in clinical medical imaging. In this paper we address this problem by defining an MDTF design concept based on improved histogram display and interaction in an exploratory process. To this end we propose sorted histograms, 2D histograms that retain the intuitive appearance of a traditional 1D histogram while conveying a second attribute. We deploy the histograms in medical visualizations using data attributes capturing domain knowledge e.g. in terms of homogeneity and typical surrounding of tissues. The resulting renderings demonstrate that the proposed concept supports a vast number of visualization possibilities based on multi-dimensional attribute data.

  • 32.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Systems for visualizing images using explicit quality prioritization of a feature (s) in multidimensional image data sets, related methods and computer products2010Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Visualization systems for rendering images from a multi-dimensional data set, include an interactive visualization system configured to accept user input to define at least one explicit prioritized feature in an image rendered from a multi-dimensional image data set. The at least one prioritized feature is automatically electronically rendered with high or full quality in different interactively requested rendered images of the image data while other non-prioritized features are rendered at lower quality. The visualization system may optionally include a rendering system configured to render images by electronically assigning a level of detail for different tiles associated with an image, each level of detail having a number of pixel samples to be calculated to thereby accelerate image processing.

  • 33.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation2007In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 13, no 6, p. 1648-1655Article in journal (Refereed)
    Abstract [en]

    Direct volume rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

  • 34.
    Lundström, Claes
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Characterizing visual analytics in diagnostic imaging2011In: EuroVA 2011: International Workshop on Visual Analytics, 2011, p. 1-4Conference paper (Other academic)
    Abstract [en]

    Many necessary and desired improvements in healthcare are dependent on progress in medical imaging. As shown in this paper, the challenges targeted by visual analytics (VA) coincide with main challenges for radiologists' diagnostic work. Key prerequisites for VA in this application domain have been identified through analysis of a survey among 22 radiologists at a university hospital. Two major findings are that efficiency is perceived as the most challenging aspect of their diagnostic work and that an exploratory approach is necessary in everyday image review. The presented characterization constitutes a validated input for design of future VA research initiatives within medical imaging.

  • 35.
    Lundström, Claes
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Ross, Steffen
    Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Ljung, Patric
    Siemens Corporate Research, Princeton, NJ, USA.
    Lindholm, Stefan
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Gyllensvärd, Frida
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    State-of-the-art of visualization in post-mortem imaging2012In: Acta Pathologica, Microbiologica et Immunologica Scandinavica (APMIS), ISSN 0903-4641, E-ISSN 1600-0463, Vol. 120, no 4, p. 316-326Article, review/survey (Refereed)
    Abstract [en]

    Autopsies constitute a valuable feedback to the healthcare chain to achieve improvements in quality of care and cost effectiveness. This review describes post-mortem imaging, which has emerged as an important part of the pathology toolbox. A broad range of visualization aspects within post-mortem imaging are covered. General state-of-the-art overviews of the components in the visualization pipeline are complemented by in-depth descriptions of methods developed by the authors and our collaborators. The forensic field is represented and related to, as it is spearheading much development in postmortem imaging. Other topics are workflow, imaging data acquisition, and visualization rendering technology. All in all, this review shows the mature state of visual analysis for a non-or minimal-invasive investigation of the deceased patient.

  • 36.
    Lundström, Claes
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Rydell, Thomas
    Interact Institute, Norrköping.
    Forsell, Camilla
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Multi-Touch Table System for Medical Visualization: Application to Orthopedic Surgery Planning2011In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 17, no 12, p. 1775-1784Article in journal (Refereed)
    Abstract [en]

    Medical imaging plays a central role in a vast range of healthcare practices. The usefulness of 3D visualizations has been demonstrated for many types of treatment planning. Nevertheless, full access to 3D renderings outside of the radiology department is still scarce even for many image-centric specialties. Our work stems from the hypothesis that this under-utilization is partly due to existing visualization systems not taking the prerequisites of this application domain fully into account. We have developed a medical visualization table intended to better fit the clinical reality. The overall design goals were two-fold: similarity to a real physical situation and a very low learning threshold. This paper describes the development of the visualization table with focus on key design decisions. The developed features include two novel interaction components for touch tables. A user study including five orthopedic surgeons demonstrates that the system is appropriate and useful for this application domain.

  • 37.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Sectra AB.
    Thorstenson, Sten
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Waltersson, Marie
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Treanor, Darren
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. St. James University Hospital, Leeds, England.
    Summary of 2nd Nordic symposium on digital pathology2015In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 6Article in journal (Refereed)
    Abstract [en]

    Techniques for digital pathology are envisioned to provide great benefits in clinical practice, but experiences also show that solutions must be carefully crafted. The Nordic countries are far along the path toward the use of whole-slide imaging in clinical routine. The Nordic Symposium on Digital Pathology (NDP) was created to promote knowledge exchange in this area, between stakeholders in health care, industry, and academia. This article is a summary of the NDP 2014 symposium, including conclusions from a workshop on clinical adoption of digital pathology among the 144 attendees.

  • 38.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Sweden.
    Waltersson, Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). University of Leeds, UK; St. James University Hospital, Leeds, UK.
    Summary of the 4th Nordic Symposium on Digital Pathology2017In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 8Article in journal (Other academic)
    Abstract [en]

    The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange across stakeholders in health care, industry, and academia. In 2016, the 4th NDP installment took place in Linköping, Sweden, promoting development and collaboration in digital pathology for the benefit of routine care advances. This article summarizes the symposium, gathering 170 attendees from 13 countries. This summary also contains results from a survey on integrated diagnostics aspects, in particular radiology-pathology collaboration.

  • 39.
    Lundström, Claes
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Waltersson, Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Department of Cellular Pathology, St. James University Hospital, Leeds, UK.
    Summary of third Nordic symposium on digital pathology2016In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 7, no 12Article in journal (Other academic)
    Abstract [en]

    Cross-disciplinary and cross-sectorial collaboration is a key success factor for turning the promise of digital pathology into actual clinical benefits. The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange in this area, among stakeholders in health care, industry, and academia. This article is a summary of the third NDP symposium in Linkφping, Sweden. The Nordic experiences, including several hospitals using whole-slide imaging for substantial parts of their primary reviews, formed a fertile base for discussions among the 190 NDP attendees originating from 15 different countries. This summary also contains results from a survey on adoption and validation aspects of clinical digital pathology use.

  • 40.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology.
    Ynnerman, Anders
    Linköping University.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology.
    Method for reducing the amount of data to be processed in a visualization pipeline2011Patent (Other (popular science, discussion, etc.))
  • 41.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    The alpha-histogram: Using Spatial Coherence to Enhance Histograms and Transfer Function Design2006In: Proceedings Eurographics/IEEE Symposium on Visualization 2006, Lisbon, Portugal, 2006, p. 227-234Conference paper (Other academic)
    Abstract [en]

    The high complexity of Transfer Function (TF) design is a major obstacle to widespread routine use of Direct Volume Rendering, particularly in the case of medical imaging. Both manual and automatic TF design schemes would benefit greatly from a fast and simple method for detection of tissue value ranges. To this end, we introduce the a-histogram, an enhancement that amplifies ranges corresponding to spatially coherent materials. The properties of the a-histogram have been explored for synthetic data sets and then successfully used to detect vessels in 20 Magnetic Resonance angiographies, proving the potential of this approach as a fast and simple technique for histogram enhancement in general and for TF construction in particular.

  • 42.
    Molin, Jesper
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). t2i Interaction Laboratory, Chalmers University of Technology.
    Fjeld, Morten
    t2i Interaction Laboratory, Chalmers University of Technology.
    Mello-Thoms, Claudia
    Medical Radiation Sciences, University of Sydney.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Slide navigation patterns among pathologists with long experience of digital review2015In: Histopathology, ISSN 0309-0167, E-ISSN 1365-2559, Vol. 67, no 2, p. 185-192Article in journal (Refereed)
    Abstract [en]

    Aims

    In order to develop efficient digital pathology workstations, we studied the navigation patterns of pathologists diagnosing whole-slide images. To gain a better understanding of these patterns, we built a conceptual model based on observations. We also determined whether or not new navigation patterns have emerged among pathologists with extensive digital experience.

    Methods and results

    Five pathologists were asked to diagnose a set of four cases while thinking out loud. The navigation within the digital slides was recorded and divided into re-occurring navigation actions. The pathologists reused the same type of actions, but their occurrence differed. The most common action was a slow panning that followed an edge structure or covered an area systematically, which accounted for 30.2% of all actions and had a median duration of 7.2 s. Of all the actions, 49% were carried out within the navigation overview and 38% of the actions could not have been performed with a conventional microscope.

    Conclusions

    The new navigation possibilities in the digital workstation were used to a large extent. The division of actions into different concepts can be used to find and prioritize between existing user interface designs as well as to understand the different navigation styles used by different pathologists.

  • 43.
    Molin, Jesper
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Chalmers University of Technology, Gothenburg, Sweden.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Sectra AB.
    Fjeld, Morten
    Chalmers University of Technology, Gothenburg, Sweden.
    A comparative study of input devices for digital slide navigation2015In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 6Article in journal (Refereed)
    Abstract [en]

    This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Quick and seamless integration between input devices and the navigation of digital slides remains a key barrier for many pathologists to “go digital.” To better understand this integration, three different input device implementations were compared in terms of time to diagnose, perceived workload and users’ preferences. Six pathologists reviewed in total nine cases with a computer mouse, a 6 degrees-of-freedom (6DOF) navigator and a touchpad. The participants perceived significantly less workload (P < 0.05) with the computer mouse and the 6DOF navigator, than with the touchpad, while no effect of the input device used on the time to diagnose was observed. Five out of six pathologists preferred the 6DOF navigator, while the touchpad was the least preferred device. While digital slide navigation is often designed to mimic microscope interaction, the results of this study demonstrate that in order to minimize workload there is reason to let the digital interaction move beyond the familiar microscope tradition.

  • 44.
    Molin, Jesper
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Chalmers University of Technology, Gothenburg, Sweden.
    Shaga Devan, Kavitha
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, The Institute of Technology.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Feature-enhancing zoom to facilitate Ki-67 hot spot detection2014In: Medical Imaging 2014: Digital Pathology, SPIE - International Society for Optical Engineering, 2014, Vol. 9041, p. Art.nr. 90410W-Conference paper (Refereed)
    Abstract [en]

    Image processing algorithms in pathology commonly include automated decision points such as classifications. While this enables efficient automation, there is also a risk that errors are induced. A different paradigm is to use image processing for enhancements without introducing explicit classifications. Such enhancements can help pathologists to increase efficiency without sacrificing accuracy. In our work, this paradigm has been applied to Ki-67 hot spot detection. Ki-67 scoring is a routine analysis to quantify the proliferation rate of tumor cells. Cell counting in the hot spot, the region of highest concentration of positive tumor cells, is a method increasingly used in clinical routine. An obstacle for this method is that while hot spot selection is a task suitable for low magnification, high magnification is needed to discern positive nuclei, thus the pathologist must perform many zooming operations. We propose to address this issue by an image processing method that increases the visibility of the positive nuclei at low magnification levels. This tool displays the modified version at low magnification, while gradually blending into the original image at high magnification. The tool was evaluated in a feasibility study with four pathologists targeting routine clinical use. In a task to compare hot spot concentrations, the average accuracy was 75±4.1% using the tool and 69±4.6% without it (n=4). Feedback on the system, gathered from an observer study, indicate that the pathologists found the tool useful and fitting in their existing diagnostic process. The pathologists judged the tool to be feasible for implementation in clinical routine.

  • 45.
    Molin, Jesper
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Chalmers University of Technology, Gothenburg, Sweden; Sectra AB, Linkoping, Sweden.
    Wozniak, Pawel W.
    Chalmers University of Technology, Gothenburg, Sweden; University of Stuttgart, Germany.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linkoping, Sweden.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Leeds Teaching Hospital NHS Trust Leeds, England.
    Fjeld, Morten
    Chalmers University of Technology, Gothenburg, Sweden.
    Understanding Design for Automated Image Analysis in Digital Pathology2016In: PROCEEDINGS OF THE NORDICHI 16: THE 9TH NORDIC CONFERENCE ON HUMAN-COMPUTER INTERACTION - GAME CHANGING DESIGN, Association for Computing Machinery (ACM), 2016, article id 58Conference paper (Refereed)
    Abstract [en]

    Digital pathology is an emerging healthcare field taking advantage of technology that allows digitization of microscopy images. Such digitization enables the use of automated digital image analysis techniques, which could be beneficial for the diagnostic review and prognosis of a variety of conditions. As yet, human-computer interaction (HCI) issues in this field, which is mostly based on visual analysis, have not been systematically explored. Based on reflecting on the process of designing and deploying systems for digital pathology, we propose a new understanding to design automated tools for such environments. We used meeting minutes, design briefs, interviews, personal notes and other artifacts to conduct a thematic analysis. This enabled us to establish four design considerations for introducing digital image analysis to routine pathology that concern level of detail, verification, communication and transparency.

  • 46.
    Persson, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Brismar, Torkel
    Karolinska Institutet, CLINTEC, Röntgenavdelningen, Karolinska Universitetssjukhuset Huddinge.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Othberg, Fredrik
    Linköping University, Department of Biomedical Engineering, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Standardized volume rendering for magnetic resonance angiography measurements in the abdominal aorta2006In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 47, no 2, p. 172-178Article in journal (Refereed)
    Abstract [en]

    Purpose: To compare three methods for standardizing volume rendering technique (VRT) protocols by studying aortic diameter measurements in magnetic resonance angiography (MRA) datasets.

    Material and Methods: Datasets from 20 patients previously examined with gadolinium-enhanced MRA and with digital subtraction angiography (DSA) for abdominal aortic aneurysm were retrospectively evaluated by three independent readers. The MRA datasets were viewed using VRT with three different standardized transfer functions: the percentile method (Pc-VRT), the maximum-likelihood method (ML-VRT), and the partial range histogram method (PRH-VRT). The aortic diameters obtained with these three methods were compared with freely chosen VRT parameters (F-VRT) and with maximum intensity projection (MIP) concerning inter-reader variability and agreement with the reference method DSA.

    Results: F-VRT parameters and PRH-VRT gave significantly higher diameter values than DSA, whereas Pc-VRT gave significantly lower values than DSA. The highest interobserver variability was found for F-VRT parameters and MIP, and the lowest for Pc-VRT and PRH-VRT. All standardized VRT methods were significantly superior to both MIP and F-VRT in this respect. The agreement with DSA was best for PRH-VRT, which was the only method with a mean error below 1 mm and which also had the narrowest limits of agreement (95% of cases between 2.1 mm below and 3.1 mm above DSA).

    Conclusion: All the standardized VRT methods compare favorably with MIP and VRT with freely selected parameters as regards interobserver variability. The partial range histogram method, although systematically overestimating vessel diameters, gives results closest to those of DSA.

  • 47.
    Scandurra, Isabella
    et al.
    APRI|eHealth AB, Örnsköldsvik, Sweden.
    Forsell, Camilla
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Advancing the state-of-the-art for Virtual Autopsies--initial forensic workflow study2010In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 160, p. 639-643Article in journal (Refereed)
    Abstract [en]

    There are numerous advantages described of how imaging technology can support forensic examinations. However, postmortem examinations of bodies are mainly performed to address demands which differ from those of traditional clinical image processing. This needs to be kept in mind when gathering information from image data sets for forensic purposes. To support radiologists and forensic clinicians using Virtual Autopsy technologies, an initial workflow study regarding post-mortem imaging has been performed, aiming to receive an improved understanding of how Virtual Autopsy workstations, image data sets and processes can be adjusted to support and improve conventional autopsies. This paper presents potential impacts and a current forensic Virtual Autopsy workflow aiming to form a foundation for collaborative procedures that increase the value of Virtual Autopsy. The workflow study will provide an increased and mutual understanding of involved professionals. In addition, insight into future forensic workflows based on demands from both forensic and radiologist perspectives bring visualization and medical informatics researchers together to develop and improve the technology and software needed.

  • 48.
    Thorstenson, Sten
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Pathology and Clinical Genetics.
    Molin, Jesper
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Chalmers University of Technology, Göteborg.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Implementation of large‑scale routine diagnostics using whole slideimaging in Sweden: Digital pathology experiences 2006-20132014In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 5, no 14Article in journal (Refereed)
    Abstract [en]

    Recent technological advances have improved the whole slide imaging (WSI) scanner quality and reduced the cost of storage, thereby enabling the deployment of digital pathology for routine diagnostics. In this paper we present the experiences from two Swedish sites having deployed routine large-scale WSI for primary review. At Kalmar County Hospital, the digitization process started in 2006 to reduce the time spent at the microscope in order to improve the ergonomics. Since 2008, more than 500,000 glass slides have been scanned in the routine operations of Kalmar and the neighboring Linköping University Hospital. All glass slides are digitally scanned yet they are also physically delivered to the consulting pathologist who can choose to review the slides on screen, in the microscope, or both. The digital operations include regular remote case reporting by a few hospital pathologists, as well as around 150 cases per week where primary review is outsourced to a private clinic. To investigate how the pathologists choose to use the digital slides, a web-based questionnaire was designed and sent out to the pathologists in Kalmar and Linköping. The responses showed that almost all pathologists think that ergonomics have improved and that image quality was sufficient for most histopathologic diagnostic work. 38 ± 28% of the cases were diagnosed digitally, but the survey also revealed that the pathologists commonly switch back and forth between digital and conventional microscopy within the same case. The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today.

  • 49.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). KTH Royal Institute Technology, Sweden; Sectra AB, S-58330 Linkoping, Sweden.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Fransson, Sven Göran
    Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). KTH Royal Institute Technology, Sweden.
    Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm2015In: Journal of Medical Imaging and Health Informatics, ISSN 2156-7018, E-ISSN 2156-7026, Vol. 5, no 8, p. 1998-2002Article in journal (Refereed)
    Abstract [en]

    A new level-set based interactive segmentation framework is introduced, where the algorithm learns the intensity distributions of the tumor and surrounding tissue from a line segment drawn by the user from the middle of the lesion towards the border. This information is used to design a likelihood function, which is then incorporated into the level-set framework as an external speed function guiding the segmentation. The endpoint of the input line segment sets a limit to the propagation of 3D region, i.e., when the zero-level-set crosses this point, the propagation is forced to stop. Finally, a fast level set algorithm with coherent propagation is used to solve the level set equation in real time. This allows the user to instantly see the 3D result while adjusting the position of the line segment to tune the parameters implicitly. The "fluctuating" character of the coherent propagation also enables the contour to coherently follow the mouse cursors motion when the user tries to fine-tune the position of the contour on the boundary, where the learned likelihood function may not necessarily change much. Preliminary results suggest that radiologists can easily learn how to use the proposed segmentation tool and perform relatively accurate segmentation with much less time than the conventional slice-by-slice based manual procedure.

  • 50.
    Wang, Chunliang
    et al.
    Sectra, KTH Royal Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    CT scan range estimation using multiple body parts detection: let PACS learn the CT image content2016In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 39, no 2, p. 149-159Article in journal (Refereed)
    Abstract [en]

    Purpose

    The aim of this study was to develop an efficient CT scan range estimation method that is based on the analysis of image data itself instead of metadata analysis. This makes it possible to quantitatively compare the scan range of two studies.

    Methods

    In our study, 3D stacks are first projected to 2D coronal images via a ray casting-like process. Trained 2D body part classifiers are then used to recognize different body parts in the projected image. The detected candidate regions go into a structure grouping process to eliminate false-positive detections. Finally, the scale and position of the patient relative to the projected figure are estimated based on the detected body parts via a structural voting. The start and end lines of the CT scan are projected to a standard human figure. The position readout is normalized so that the bottom of the feet represents 0.0, and the top of the head is 1.0.

    Results

    Classifiers for 18 body parts were trained using 184 CT scans. The final application was tested on 136 randomly selected heterogeneous CT scans. Ground truth was generated by asking two human observers to mark the start and end positions of each scan on the standard human figure. When compared with the human observers, the mean absolute error of the proposed method is 1.2 % (max: 3.5 %) and 1.6 % (max: 5.4 %) for the start and end positions, respectively.

    Conclusion

    We proposed a scan range estimation method using multiple body parts detection and relative structure position analysis. In our preliminary tests, the proposed method delivered promising results.

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