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Asa, S., Bodén, A., Treanor, D., Jarkman, S., Lundström, C. & Pantatnowitz, L. (2019). 2020 vision of digital pathology in action. Journal of Pathology Informatics, 10(27)
Open this publication in new window or tab >>2020 vision of digital pathology in action
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2019 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 10, no 27Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Medknow Publications, 2019
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-160147 (URN)10.4103/jpi.jpi_31_19 (DOI)31516758 (PubMedID)
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-11-27Bibliographically approved
Skoglund, K., Rose, J., Lindvall, M., Lundström, C. & Treanor, D. (2019). Annotations, ontologies, and whole slide images: Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue. Journal of Pathology Informatics, 10(22)
Open this publication in new window or tab >>Annotations, ontologies, and whole slide images: Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue
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2019 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 10, no 22Article in journal (Refereed) Published
Abstract [en]

Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. 

Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. 

Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. 

Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

Place, publisher, year, edition, pages
Medknow Publications, 2019
Keywords
Annotation, digital pathology, image database, ontology, whole slide images
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-160146 (URN)10.4103/jpi.jpi_81_18 (DOI)
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-11-27Bibliographically approved
Falk, M., Ynnerman, A., Treanor, D. & Lundström, C. (2019). Interactive Visualization of 3D Histopathology in Native Resolution. IEEE Transactions on Visualization and Computer Graphics, 25(1), 1008-1017
Open this publication in new window or tab >>Interactive Visualization of 3D Histopathology in Native Resolution
2019 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 1, p. 1008-1017Article in journal (Refereed) Published
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.

Keywords
Histology, Pathology, Volume Rendering, Expert Evaluation
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-150420 (URN)10.1109/tvcg.2018.2864816 (DOI)000452640000096 ()
Funder
VINNOVA, 2014-04257
Note

Funding agencies: Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish e-Science Research Centre (SeRC); DigiPat project by VINNOVA grant [2014-04257]

Available from: 2018-08-22 Created: 2018-08-22 Last updated: 2019-01-07
Chow, J. A., Törnros, M. E., Waltersson, M., Richard, H., Kusoffsky, M., Lundström, C. & Kurti, A. (2017). A Design Study Investigating Augmented Reality and Photograph Annotation in a Digitalized Grossing Workstation. Journal of Pathology Informatics, 8(31)
Open this publication in new window or tab >>A Design Study Investigating Augmented Reality and Photograph Annotation in a Digitalized Grossing Workstation
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2017 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 8, no 31Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Medknow Publications, 2017
Keywords
Augmented reality; design methods; gross pathology; human–computer interaction; interface design; visualization
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-145037 (URN)10.4103/jpi.jpi_13_17 (DOI)28966831 (PubMedID)
Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-05-07Bibliographically approved
Wang, C. & Lundström, C. (2016). CT scan range estimation using multiple body parts detection: let PACS learn the CT image content. International Journal of Computer Assisted Radiology and Surgery, 39(2), 149-159
Open this publication in new window or tab >>CT scan range estimation using multiple body parts detection: let PACS learn the CT image content
2016 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2016
Keywords
Scan range estimation, Body parts detection, Structural voting, Machine learning, Pictorial structures, Image classification
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-120486 (URN)10.1007/s11548-015-1232-z (DOI)000370160300015 ()26137895 (PubMedID)
Available from: 2015-08-11 Created: 2015-08-11 Last updated: 2019-11-07Bibliographically approved
Cervin, I., Molin, J. & Lundström, C. (2016). Improving the creation and reporting of structured findings during digital pathology review. Journal of Pathology Informatics, 7(1), 32-32
Open this publication in new window or tab >>Improving the creation and reporting of structured findings during digital pathology review
2016 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 7, no 1, p. 32-32Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Medknow Publications, 2016
Keywords
Digital pathology, structured reporting, usability, workflow
National Category
Other Medical Engineering Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-130541 (URN)10.4103/2153-3539.186917 (DOI)27563491 (PubMedID)2-s2.0-85009264084 (Scopus ID)
Available from: 2016-08-15 Created: 2016-08-15 Last updated: 2018-01-10Bibliographically approved
Lundström, C., Waltersson, M., Persson, A. & Treanor, D. (2016). Summary of third Nordic symposium on digital pathology. Journal of Pathology Informatics, 7(12)
Open this publication in new window or tab >>Summary of third Nordic symposium on digital pathology
2016 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 7, no 12Article in journal, Editorial material (Other academic) Published
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.

Place, publisher, year, edition, pages
Medknow Publications, 2016
Keywords
Implementation; pathology informatics; validation
National Category
Other Medical Engineering Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-130542 (URN)10.4103/2153-3539.179902 (DOI)27141318 (PubMedID)2-s2.0-85009253042 (Scopus ID)
Funder
VINNOVA, 2012-01121
Available from: 2016-08-15 Created: 2016-08-15 Last updated: 2018-01-10Bibliographically approved
Molin, J., Lundström, C. & Fjeld, M. (2015). A comparative study of input devices for digital slide navigation. Journal of Pathology Informatics, 6
Open this publication in new window or tab >>A comparative study of input devices for digital slide navigation
2015 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 6Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Medknow Publications, 2015
National Category
Human Computer Interaction Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-118882 (URN)10.4103/2153-3539.151894 (DOI)25774318 (PubMedID)
Funder
Swedish Research CouncilVINNOVA
Available from: 2015-06-04 Created: 2015-06-04 Last updated: 2018-01-11
Ynnerman, A., Rydell, T., Persson, A., Ernvik, A., Forsell, C., Ljung, P. & Lundström, C. (2015). Multi-Touch Table System for Medical Visualization. In: Eurographics 2015: Dirk Bartz Prize. Paper presented at Eurographics 2015. Eurographics - European Association for Computer Graphics
Open this publication in new window or tab >>Multi-Touch Table System for Medical Visualization
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2015 (English)In: Eurographics 2015: Dirk Bartz Prize, Eurographics - European Association for Computer Graphics, 2015Conference paper, Published paper (Other academic)
Abstract [en]

Medical imaging plays a central role in a vast range of healthcare practices. While the usefulness of 3D visualizations is well known, the adoption of such technology has previously been limited in many medical areas. This paper, awarded the Dirk Bartz Prize for Visual Computing in Medicine 2015, describes the development of a medical multi-touch visualization table that successfully has reached its aim to bring 3D visualization to a wider clinical audience. The descriptions summarize the targeted clinical scenarios, the key characteristics of the system, and the user feedback obtained.

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2015
National Category
Other Medical Engineering Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-130543 (URN)10.2312/egm.20151030 (DOI)
Conference
Eurographics 2015
Available from: 2016-08-15 Created: 2016-08-15 Last updated: 2018-01-10
Wang, C., Dahlström, N., Fransson, S. G., Lundström, C. & Smedby, Ö. (2015). Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm. Journal of Medical Imaging and Health Informatics, 5(8), 1998-2002
Open this publication in new window or tab >>Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm
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2015 (English)In: Journal of Medical Imaging and Health Informatics, ISSN 2156-7018, E-ISSN 2156-7026, Vol. 5, no 8, p. 1998-2002Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
AMER SCIENTIFIC PUBLISHERS, 2015
Keywords
Interactive Image Segmentation; Level Set; Coherent Propagation; Tumor Segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-125166 (URN)10.1166/jmihi.2015.1685 (DOI)000368564700072 ()
Available from: 2016-02-15 Created: 2016-02-15 Last updated: 2018-01-10
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-0177

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