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  • 1.
    Andersson, Malin
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Jägervall, Karl
    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).
    Eriksson, Per
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Region Östergötland, Heart and Medicine Center, Department of Rheumatology. Linköping University, Faculty of Medicine and Health Sciences.
    Persson, Anders
    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).
    Granerus, Göran
    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 Clinical Physiology in Linköping.
    Wang, Chunliang
    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.
    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.
    How to measure renal artery stenosis - a retrospective comparison of morphological measurement approaches in relation to hemodynamic significance2015In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, no 42Article in journal (Refereed)
    Abstract [en]

    Background: Although it is well known that renal artery stenosis may cause renovascular hypertension, it is unclear how the degree of stenosis should best be measured in morphological images. The aim of this study was to determine which morphological measures from Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) are best in predicting whether a renal artery stenosis is hemodynamically significant or not. Methods: Forty-seven patients with hypertension and a clinical suspicion of renovascular hypertension were examined with CTA, MRA, captopril-enhanced renography (CER) and captopril test (Ctest). CTA and MRA images of the renal arteries were analyzed by two readers using interactive vessel segmentation software. The measures included minimum diameter, minimum area, diameter reduction and area reduction. In addition, two radiologists visually judged the diameter reduction without automated segmentation. The results were then compared using limits of agreement and intra-class correlation, and correlated with the results from CER combined with Ctest (which were used as standard of reference) using receiver operating characteristics (ROC) analysis. Results: A total of 68 kidneys had all three investigations (CTA, MRA and CER + Ctest), where 11 kidneys (16.2 %) got a positive result on the CER + Ctest. The greatest area under ROC curve (AUROC) was found for the area reduction on MRA, with a value of 0.91 (95 % confidence interval 0.82-0.99), excluding accessory renal arteries. As comparison, the AUROC for the radiologists visual assessments on CTA and MRA were 0.90 (0.82-0.98) and 0.91 (0.83-0.99) respectively. None of the differences were statistically significant. Conclusions: No significant differences were found between the morphological measures in their ability to predict hemodynamically significant stenosis, but a tendency of MRA having higher AUROC than CTA. There was no significant difference between measurements made by the radiologists and measurements made with fuzzy connectedness segmentation. Further studies are required to definitely identify the optimal measurement approach.

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  • 2.
    Kirişli, Hortense
    et al.
    Erasmus MC, Rotterdam, The Netherlands.
    Schaap, M.
    Erasmus MC, Rotterdam, The Netherlands.
    Metz, C. T.
    Erasmus MC, Rotterdam, The Netherlands.
    Dharampal, A. S.
    Erasmus MC, Rotterdam, The Netherlands.
    Meijboom, W. B.
    Erasmus MC, Rotterdam, The Netherlands.
    Papadopoulou, S. L.
    Erasmus MC, Rotterdam, The Netherlands.
    Dedic, A.
    Erasmus MC, Rotterdam, The Netherlands.
    Nieman, K.
    Erasmus MC, Rotterdam, The Netherlands.
    de Graaf, M. A.
    Leiden UMC, The Netherlands.
    Meijs, M. F. L.
    UMC Utrecht, The Netherlands.
    Cramer, M. J.
    UMC Utrecht, The Netherlands.
    Broersen, A.
    Leiden UMC, The Netherlands.
    Cetin, S.
    SabancıUniversity, Turkey.
    Eslami, A.
    Technical University of Munich, Germany.
    Flórez-Valencia, L.
    Pontificia Universidad Javeriana, Bogotá, Colombia.
    Lor, K.L.
    National Taiwan University, Taipei, Taiwan.
    Matuszewski, B.
    University of Central Lancashire, Preston, UK.
    Melki, I.
    Université Paris-Est, France.
    Mohr, B.
    Toshiba Medical Visualization Systems, Edinburgh, UK.
    Öksüz, I.
    Bahçeşehir University, Istanbul, Turkey.
    Shahzad, R.
    Erasmus MC, Rotterdam The Netherlands.
    Wang, Chunliang
    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 Health Sciences.
    Kitslaar, P. H.
    Leiden UMC, The Netherlands.
    Unal, G.
    Sabancı University, Turkey.
    Katouzian, A.
    Technical University of Munich, Germany.
    Orkisz, M.
    Université de Lyon, France.
    Chen, C.M.
    National Taiwan University, Taipei, Taiwan.
    Precioso, F.
    University Nice-Sophia Antipolis, France.
    Najman, L.
    Université Paris-Est, France.
    Masood, S.
    Toshiba Medical Visualization Systems, Edinburgh, UK.
    Ünay, D.
    Bahçeşehir University, Istanbul, Turkey.
    van Vliet, L.
    Delft University of Technology, The Netherlands.
    Moreno, Rodrigo
    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 Health Sciences.
    Goldenberg, R.
    Rcadia Medical Imaging, Haïfa, Israel.
    Vuçini, E.
    VRVis Research Center for Virtual Reality and Visualization, Vienna, Austria.
    Krestin, G. P.
    Erasmus MC, Rotterdam, The Netherlands.
    Niessen, W. J.
    Erasmus MC, Rotterdam, The Netherlands.
    van Walsum, T.
    Erasmus MC, Rotterdam, The Netherlands.
    Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography2013In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 17, no 8, p. 859-876Article in journal (Refereed)
    Abstract [en]

    Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

  • 3.
    Lidayova, Kristina
    et al.
    Uppsala University, Sweden.
    Frimmel, Hans
    Uppsala University, Sweden.
    Wang, Chunliang
    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.
    Bengtsson, Ewert
    Uppsala University, Sweden.
    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.
    Fast vascular skeleton extraction algorithm2016In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, p. 67-75Article in journal (Refereed)
    Abstract [en]

    Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task. In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29 s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%. (C) 2015 Elsevier B.V. All rights reserved.

  • 4.
    Maria Marreiros, Filipe Miguel
    et al.
    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.
    Rossitti, Sandro
    Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Karlsson, Per
    Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Wang, Chunliang
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge.
    Gustafsson, Torbjörn
    XM Reality AB, Linköping, Sweden.
    Carleberg, Per
    XM Reality AB, Linköping, Sweden.
    Smedby, Örjan
    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. 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. Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge .
    Superficial vessel reconstruction with a multiview camera system2016In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 3, no 1, p. 015001-1-015001-13Article in journal (Refereed)
    Abstract [en]

    We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformationmethods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstructionusing three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlinesare manually selected in the images. Using the properties of the Hessian matrix, the centerline points areassigned direction information. For correspondence matching, a combination of methods was used. The processstarts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labelingand an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline withdecreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation invirtual, phantom, and experimental images, including intraoperative data from patient experiments, showsthat, with appropriate camera positions, the error estimates (root-mean square error and mean error) are∼1 mm.

  • 5.
    Maria Marreiros, Filipe Miguel
    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. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    Rossitti, Sandro
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Wang, Chunliang
    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. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    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. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Non-rigid Deformation Pipeline for Compensation of Superficial Brain Shift2013In: Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II, Springer Berlin/Heidelberg, 2013, p. 141-148Conference paper (Refereed)
    Abstract [en]

    The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.

  • 6.
    Maria Marreiros, Filipe Miguel
    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. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Wang, Chunliang
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Royal Institute of Technology (KTH), School of Technology and Health, Huddinge, Sweden.
    Rossitti, Sandro
    Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Smedby, Örjan
    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. Royal Institute of Technology (KTH), School of Technology and Health, Huddinge, Sweden.
    Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain2016In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE - International Society for Optical Engineering, 2016, Vol. 9786, p. 8p. 978611-1-978611-8Conference paper (Refereed)
    Abstract [en]

    In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). Themethod was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to matchvessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-PlateSpline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, whilethe semilandmark method iteratively relaxes/slides the points.

    For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were appliedto the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where afew control points were manipulated to obtain the desired transformation (T1). Once the correspondences areknown, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in thedeformed space, by transforming the original points using T1 and T2 and measuring the distance between them.To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and thendeformed. Furthermore, anisotropic normally distributed noise was added.

    The results show that the error estimates (root mean square error and mean error) are below 1 mm, even inthe presence of noise and incomplete data.

  • 7.
    Medrano-Gracia, Pau
    et al.
    Dept. Anatomy with Radiology, University of Auckland, New Zealand.
    Ormiston, John
    Auckland Heart Group, Auckland, New Zealand.
    Webster, Mark
    Auckland City Hospital, Auckland, New Zealand.
    Beier, Susann
    Dept. Anatomy with Radiology, University of Auckland, New Zealand.
    Ellis, Chris
    Auckland Heart Group, Auckland, New Zealand.
    Wang, Chunliang
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Young, Alistair A
    Dept. Anatomy with Radiology, University of Auckland, New Zealand.
    Cowan, Brett R
    Dept. Anatomy with Radiology, University of Auckland, New Zealand.
    Construction of a coronary artery atlas from CT angiography.2014In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Springer, 2014, Vol. 8674, no 2014, p. 513-520Conference paper (Refereed)
    Abstract [en]

    Describing the detailed statistical anatomy of the coronary artery tree is important for determining the aetiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.

  • 8.
    Mendrik, Adrienne M.
    et al.
    University of Medical Centre Utrecht, Netherlands.
    Vincken, Koen L.
    University of Medical Centre Utrecht, Netherlands.
    Kuijf, Hugo J.
    University of Medical Centre Utrecht, Netherlands.
    Breeuwer, Marcel
    Philips Healthcare, Netherlands; Eindhoven University of Technology, Netherlands.
    Bouvy, Willem H.
    University of Medical Centre Utrecht, Netherlands.
    de Bresser, Jeroen
    University of Medical Centre Utrecht, Netherlands.
    Alansary, Amir
    University of Louisville, KY 40292 USA.
    de Bruijne, Marleen
    Erasmus MC, Netherlands; Erasmus MC, Netherlands; University of Copenhagen, Denmark.
    Carass, Aaron
    Johns Hopkins University, MD 21218 USA.
    El-Baz, Ayman
    University of Louisville, KY 40292 USA.
    Jog, Amod
    Johns Hopkins University, MD 21218 USA.
    Katyal, Ranveer
    LNM Institute Informat Technology, India.
    Khan, Ali R.
    Robarts Research Institute, Canada; University of Western Ontario, Canada.
    van der Lijn, Fedde
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Mahmood, Qaiser
    Chalmers, Sweden.
    Mukherjee, Ryan
    Johns Hopkins University, MD 20723 USA.
    van Opbroek, Annegreet
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Paneri, Sahil
    LNM Institute Informat Technology, India.
    Pereira, Sergio
    University of Minho, Portugal.
    Persson, Mikael
    Chalmers, Sweden.
    Rajchl, Martin
    Robarts Research Institute, Canada; University of London Imperial Coll Science Technology and Med, England.
    Sarikaya, Duygu
    SUNY Buffalo, NY 14260 USA.
    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).
    Silva, Carlos A.
    University of Minho, Portugal.
    Vrooman, Henri A.
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Vyas, Saurabh
    Johns Hopkins University, MD 20723 USA.
    Wang, Chunliang
    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).
    Zhao, Liang
    SUNY Buffalo, NY 14260 USA.
    Jan Biessels, Geert
    University of Medical Centre Utrecht, Netherlands.
    Viergever, Max A.
    University of Medical Centre Utrecht, Netherlands.
    MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans2015In: Computational Intelligence and Neuroscience, ISSN 1687-5265, E-ISSN 1687-5273, article id 813696Article in journal (Refereed)
    Abstract [en]

    Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

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  • 9.
    Moreno, Rodrigo
    et al.
    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 Health Sciences.
    Wang, Chunliang
    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 Health Sciences.
    Smedby, Örjan
    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 Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Vessel Wall Segmentation Using Implicit Models and  Total Curvature Penalizers2013In: IMAGE ANALYSIS, SCIA 2013: 18TH SCANDINAVIAN CONFERENCE, Springer Berlin/Heidelberg, 2013, p. 299-308Conference paper (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the 18th Scandinavian Conference on Image Analysis, SCIA 2013, held in Espoo, Finland, in June 2013. The 67 revised full papers presented were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections on feature extraction and segmentation, pattern recognition and machine learning, medical and biomedical image analysis, faces and gestures, object and scene recognition, matching, registration, and alignment, 3D vision, color and multispectral image analysis, motion analysis, systems and applications, human-centered computing, and video and multimedia analysis.

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    Vessel Wall Segmentation Using Implicit Models and Total Curvature Penalizers
  • 10.
    Petersson, Helge
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Sinkvist, David
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Wang, Chunliang
    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.
    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.
    Web-based interactive 3D visualization as a tool for improved anatomy learning2009In: Anatomical sciences education, ISSN 1935-9772, Vol. 2, no 2, p. 61-68Article in journal (Refereed)
    Abstract [en]

    Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain from its use in reaching their anatomical learning objectives. Several 3D vascular VR models were created using an interactive segmentation tool based on the "virtual contrast injection" method. This method allows users, with relative ease, to convert computer tomography or magnetic resonance images into vivid 3D VR movies using the OsiriX software equipped with the CMIV CTA plug-in. Once created using the segmentation tool, the image series were exported in Quick Time Virtual Reality (QTVR) format and integrated within a web framework of the Educational Virtual Anatomy (EVA) program. A total of nine QTVR movies were produced encompassing most of the major arteries of the body. These movies were supplemented with associated information, color keys, and notes. The results indicate that, in general, students' attitudes towards the EVA-program were positive when compared with anatomy textbooks, but results were not the same with dissections. Additionally, knowledge tests suggest a potentially beneficial effect on learning.

  • 11.
    Schaap, M.
    et al.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    Metz, C.T.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    van Walsum, T.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    van der Giessen, A.G.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    Weustink, A.C.
    Erasmus MC, Rotterdam, The Netherlands.
    Mollet, N.R.
    Erasmus MC, Rotterdam, The Netherlands.
    Bauer, C.
    Graz University of Technology, Austria.
    Bogunovic, H.
    Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Barcelona, Spain.
    Castro, C.
    Universidad Politécnica de Madrid, Spain.
    Deng, X.
    Siemens Corporate Research, Princeton, NJ, USA.
    Dikici, E.
    University of Florida College of Medicine, Jacksonville, FL, USA.
    O'Donnell, T.
    Siemens Corporate Research, Princeton, NJ, USA.
    Frenay, M.
    Leiden University Medical Center, The Netherlands.
    Friman, O.
    MeVis Research, Bremen, Germany.
    Hoyos, M.H.
    Universidad de los Andes, Bogota, Colombia.
    Kitslaar, P.H.
    Leiden University Medical Center, The Netherlands.
    Krissian, K.
    University of Las Palmas of Gran Canaria, Spain.
    Kuhnel, C.
    MeVis Research, Bremen, Germany.
    Luengo-Oroz, M.A.
    Universidad Politécnica de Madrid, Spain.
    Orkisz, M.
    Université de Lyon, France.
    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.
    Styner, M.
    University of North Carolina, Chapel Hill, NC, USA.
    Szymczak, A.
    Colorado School of Mines, Golden, CO, USA.
    Tek, H.
    Siemens Corporate Research, Princeton, NJ, USA.
    Wang, Chunliang
    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.
    Warfield, S.K.
    Childrens Hospital Boston, MA, USA.
    Zambal, S.
    VRVis Research Center for Virtual Reality and Visualization, Vienna, Austria.
    Zhang, Y.
    The Methodist Hospital Research Institute, Houston, TX, USA.
    Krestin, G.P.
    Erasmus MC, Rotterdam, The Netherlands.
    Niessen, W.J.
    Erasmus MC, Rotterdam, The Netherlands.
    Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms2009In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 13, no 5, p. 701-714Article in journal (Refereed)
    Abstract [en]

    Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

  • 12.
    Smedby, Örjan
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. 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.
    Wang, Chunliang
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Automatic Multi-organ Segmentation in Nonenhanced CT Datasets Using Hierarchical Shape Priors2014In: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE Computer Society, 2014, p. 3327-3332Conference paper (Refereed)
    Abstract [en]

    An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging, the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.

  • 13.
    Steigner, Michael L
    et al.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Mitsouras, Dimitrios
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Whitmore, Amanda G.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Otero, Hansel J.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Wang, Chunliang
    Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Buckley, Orla
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Levit, Noah A.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Hussain, Alia Z.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Cai, Tianxi
    the Department of Biostatistics, Harvard School of Public Health, Boston, MA .
    Mather, Richard T
    Toshiba Amer Med Syst, Tustin, CA USA .
    Smedby, Örjan
    Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    DiCarli, Marcelo F
    Noninvasive Cardiovascular Imaging, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA.
    Rybicki, Frank J.
    Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA .
    Iodinated Contrast Opacification Gradients in Normal Coronary Arteries Imaged With Prospectively ECG-Gated Single Heart Beat 320-Detector Row Computed Tomography2010In: CIRCULATION-CARDIOVASCULAR IMAGING, ISSN 1941-9651, Vol. 3, no 2, p. 179-186Article in journal (Refereed)
    Abstract [en]

    Background-To define and evaluate coronary contrast opacification gradients using prospectively ECG-gated single heart beat 320-detector row coronary angiography (CTA). Methods and Results-Thirty-six patients with normal coronary arteries determined by 320 x 0.5-mm detector row coronary CTA were retrospectively evaluated with customized image postprocessing software to measure Hounsfield Units at 1-mm intervals orthogonal to the artery center line. Linear regression determined correlation between mean Hounsfield Units and distance from the coronary ostium (regression slope defined as the distance gradient G(d)), lumen cross-sectional area (G(a)), and lumen short-axis diameter (G(s)). For each gradient, differences between the 3 coronary arteries were analyzed with ANOVA. Linear regression determined correlations between measured gradients, heart rate, body mass index, and cardiac phase. To determine feasibility in lesions, all 3 gradients were evaluated in 22 consecutive patients with left anterior descending artery lesions andgt;= 50% stenosis. For all 3 coronary arteries in all patients, the gradients G(a) and G(s) were significantly different from zero (P andlt; 0.0001), highly linear (Pearson r values, 0.77 to 0.84), and had no significant difference between the left anterior descending, left circumflex, and right coronary arteries (P andgt; 0.503). The distance gradient G(d) demonstrated nonlinearities in a small number of vessels and was significantly smaller in the right coronary artery when compared with the left coronary system (P andlt; 0.001). Gradient variations between cardiac phases, heart rates, body mass index, and readers were low. Gradients in patients with lesions were significantly different (P andlt; 0.021) than in patients considered normal by CTA. Conclusions-Measurement of contrast opacification gradients from temporally uniform coronary CTA demonstrates feasibility and reproducibility in patients with normal coronary arteries. For all patients, the gradients defined with respect to the coronary lumen cross-sectional area and short-axis diameters are highly linear, not significantly influenced by the coronary artery (left anterior descending artery versus left circumflex versus right coronary artery), and have only small variation with respect to patient parameters. Preliminary evaluation of gradients across coronary artery lesions is promising but requires additional study.

  • 14. Order onlineBuy this publication >>
    Wang, Chunliang
    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.
    Computer Assisted Coronary CT Angiography Analysis: Disease-centered Software Development2009Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The substantial advances of coronary CTA have resulted in a boost of use of this new technique in the last several years, which brings a big challenge to radiologists by the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing.Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” method, preserves the low density soft tissue around the coronary, which reduces the possibility of introducing false positive stenoses during segmentation.Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over¬views of the processing results. Considerable efforts were put on optimizing the computa¬tional speed of the algorithm to meet the clinical requirements.Thirdly, an automatic seeding method, that can automatically remove rib cage and recognize the aortic root, was introduced into the interactive segmentation workflow to further minimize the requirement of user interactivity during post-processing. The automatic procedure is carried out right after the images are received, which saves users time after they open the data. Vessel enhance¬ment and quantitative 2D vessel contour analysis are also included in this new version of the software. In our preliminary experience, visually accurate segmentation results of major branches have been achieved in 74 cases (42 cases reported in paper II and 32 cases in paper III) using our software with limited user interaction. On 128 branches of 32 patients, the average overlap between the centerline created in our software and the manually created reference standard was 96.0%. The average distance between them was 0.38 mm, lower than the mean voxel size. The automatic procedure ran for 3-5 min as a single-thread application in the background. Interactive processing took 3 min in average with the latest version of software. In conclusion, the presented software provides fast and automatic coron¬ary artery segmentation and visualization. The accuracy of the centerline tracking was found to be acceptable when compared to manually created centerlines.

    List of papers
    1. Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree
    Open this publication in new window or tab >>Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree
    2007 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sébastien Ourselin, Anthony Maeder, Springer Berlin/Heidelberg, 2007, Vol. 4791, p. 311-318Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2007
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4791
    Keywords
    segmentation - fuzzy connectedness tree - centerline extraction - skeletonization - coronary artery - CT angiography
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17816 (URN)10.1007/978-3-540-75757-3_38 (DOI)000250916000038 ()978-3-540-75756-6 (ISBN)
    Conference
    10th International Conference on Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, October 29 - November 2, 2007
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang and Örjan Smedby, Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree, 2007, Medical Image Computing and Computer-Assisted Intervention, (4791), 311-318. http://dx.doi.org/10.1007/978-3-540-75757-3_38 Copyright: Springer-verlag http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-02-07Bibliographically approved
    2. An interactive software module for visualizing coronary arteries in CT angiography
    Open this publication in new window or tab >>An interactive software module for visualizing coronary arteries in CT angiography
    2008 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Vol. 3, no 1-2, p. 11-18Article in journal (Refereed) Published
    Abstract [en]

    A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT).

    Materials and Methods:  The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized “virtual contrast injection” algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3–0.5 mm.

    Results:  The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches.

    Conclusion:  The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.

    Place, publisher, year, edition, pages
    Heidelberg/Berlin: Springer, 2008
    Keywords
    Coronary vessels - Tomography, spiral computed - Algorithms - Radiographic image interpretation, computer-assisted
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Cardiac and Cardiovascular Systems Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17817 (URN)10.1007/s11548-008-0160-6 (DOI)
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang, Hans Frimmel, Anders Persson and Örjan Smedby, An interactive software module for visualizing coronary arteries in CT angiography, 2008, International Journal of Computer Assisted Radiology and Surgery, (3), 1-2, 11-18. http://dx.doi.org/10.1007/s11548-008-0160-6 Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-01-13Bibliographically approved
    3. Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead
    Open this publication in new window or tab >>Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead
    2010 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 5, no 3, p. 275-285Article in journal (Refereed) Published
    Abstract [en]

    Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.

    Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.

    Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.

    Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.

    Keywords
    Coronary CT angiography, automatic vessel extraction, vessel segmentation, centerline tracking
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Cardiac and Cardiovascular Systems Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17818 (URN)10.1007/s11548-009-0393-z (DOI)000289288800008 ()
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang and Örjan Smedby, Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead, 2011, International Journal of Computer Assisted Radiology and Surgery, (5), 3, 275-285. http://dx.doi.org/10.1007/s11548-009-0393-z Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-01-13Bibliographically approved
    Download full text (pdf)
    Computer Assisted Coronary CT Angiography Analysis
  • 15. Order onlineBuy this publication >>
    Wang, Chunliang
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Computer-­Assisted  Coronary  CT  Angiography  Analysis: From  Software  Development  to  Clinical  Application2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Advances in coronary Computed Tomography Angiography (CTA) have resulted in a boost in the use of this new technique in recent years, creating a challenge for radiologists due to the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing, and to evaluate the performance in clinical settings.

    Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” (VC) method, preserves the low-density soft tissue around the artery, and thus reduces the possibility of introducing false positive stenoses during segmentation. Visually reasonable results were obtained in clinical cases.

    Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over­views of the processing results. An automatic seeding method was introduced into the interactive segmentation workflow to eliminate the requirement of user initialization during post-processing. In 42 clinical cases, all main arteries and more than 85% of visible branches were identified, and testing the centerline extraction in a reference database gave results in good agreement with the gold standard.

    Thirdly, the diagnostic accuracy of coronary CTA using the segmented 3D data from the VC method was evaluated on 30 clinical coronary CTA datasets and compared with the conventional reading method and a different 3D reading method, region growing (RG), from a commercial software. As a reference method, catheter angiography was used. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the conventional method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (p = 0.22).

    Furthermore, we developed a fast, level set-based algorithm for vessel segmentation, which is 10-20 times faster than the conventional methods without losing segmentation accuracy. It enables quantitative stenosis analysis at interactive speed.

    In conclusion, the presented software provides fast and automatic coron­ary artery segmentation and visualization. The NPV of using only segmented 3D data is as good as using conventional 2D viewing techniques, which suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. Combining the 3D visualization of segmentation data with the clinical workflow could shorten reading time.

    List of papers
    1. Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree
    Open this publication in new window or tab >>Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree
    2007 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sébastien Ourselin, Anthony Maeder, Springer Berlin/Heidelberg, 2007, Vol. 4791, p. 311-318Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2007
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4791
    Keywords
    segmentation - fuzzy connectedness tree - centerline extraction - skeletonization - coronary artery - CT angiography
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17816 (URN)10.1007/978-3-540-75757-3_38 (DOI)000250916000038 ()978-3-540-75756-6 (ISBN)
    Conference
    10th International Conference on Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, October 29 - November 2, 2007
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang and Örjan Smedby, Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree, 2007, Medical Image Computing and Computer-Assisted Intervention, (4791), 311-318. http://dx.doi.org/10.1007/978-3-540-75757-3_38 Copyright: Springer-verlag http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-02-07Bibliographically approved
    2. An interactive software module for visualizing coronary arteries in CT angiography
    Open this publication in new window or tab >>An interactive software module for visualizing coronary arteries in CT angiography
    2008 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Vol. 3, no 1-2, p. 11-18Article in journal (Refereed) Published
    Abstract [en]

    A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT).

    Materials and Methods:  The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized “virtual contrast injection” algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3–0.5 mm.

    Results:  The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches.

    Conclusion:  The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.

    Place, publisher, year, edition, pages
    Heidelberg/Berlin: Springer, 2008
    Keywords
    Coronary vessels - Tomography, spiral computed - Algorithms - Radiographic image interpretation, computer-assisted
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Cardiac and Cardiovascular Systems Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17817 (URN)10.1007/s11548-008-0160-6 (DOI)
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang, Hans Frimmel, Anders Persson and Örjan Smedby, An interactive software module for visualizing coronary arteries in CT angiography, 2008, International Journal of Computer Assisted Radiology and Surgery, (3), 1-2, 11-18. http://dx.doi.org/10.1007/s11548-008-0160-6 Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-01-13Bibliographically approved
    3. Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead
    Open this publication in new window or tab >>Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead
    2010 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 5, no 3, p. 275-285Article in journal (Refereed) Published
    Abstract [en]

    Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.

    Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.

    Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.

    Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.

    Keywords
    Coronary CT angiography, automatic vessel extraction, vessel segmentation, centerline tracking
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Cardiac and Cardiovascular Systems Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-17818 (URN)10.1007/s11548-009-0393-z (DOI)000289288800008 ()
    Note

    The original publication is available at www.springerlink.com: Chunliang Wang and Örjan Smedby, Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead, 2011, International Journal of Computer Assisted Radiology and Surgery, (5), 3, 275-285. http://dx.doi.org/10.1007/s11548-009-0393-z Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2018-01-13Bibliographically approved
    4. Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?
    Open this publication in new window or tab >>Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?
    Show others...
    2012 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 8, p. 845-851Article in journal (Refereed) Published
    Abstract [en]

    Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenoses with catheter angiography (CA) as the reference standard.

    Method: CCTA data sets from 30 patients were acquired with a 64-slice dual source CT scanner and segmented by an independent observer using the region growing (RG) method and the “virtual contrast injection” (VC) method. For every examination, each of the three types of images was  then reviewed by one of three reviewers in a blinded fashion for the presence of stenoses with diameter reduction of 50% or more. For the original series, the reviewer was allowed to use all the 2D or 3D visualization tools available (mixed method). For the segmented results (from RG and VC), the reviewer only used the 3D maximum intensity projection. Evaluation results were compared with CA for each artery.

    Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the mixed method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the mixed method (p = 0.22). Excluding vessels with heavy calcification, all three methods had similar accuracy.

    Conclusion: Diagnostic accuracy when using segmented 3D data was lower than with access to 2D images. However, the high NPV of the 3D methods suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. The VC method, which generates more evaluable arteries and has higher accuracy, seems more promising for this purpose than the RG method.

    Place, publisher, year, edition, pages
    Informa Healthcare, 2012
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-68794 (URN)10.1258/ar.2012.120053 (DOI)000310820000004 ()
    Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2021-12-28Bibliographically approved
    5. Level-set based vessel segmentation accelerated with periodic monotonic speed function
    Open this publication in new window or tab >>Level-set based vessel segmentation accelerated with periodic monotonic speed function
    2011 (English)In: Medical Imaging 2011: Image Processing / [ed] Benoit M. Dawant, David R. Haynor, SPIE - International Society for Optical Engineering, 2011, Vol. 7962, p. 79621M-1-79621M-7Conference paper, Published paper (Refereed)
    Abstract [en]

    To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour’s local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points’ expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.

    Place, publisher, year, edition, pages
    SPIE - International Society for Optical Engineering, 2011
    Series
    Progress in Biomedical Optics and Imaging, ISSN 1605-7422 ; Vol. 7962
    Keywords
    Level-set; image segmentation; monotonic speed function; coherent propagation; narrow band; sparse field
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-68006 (URN)10.1117/12.876704 (DOI)000294154900056 ()9780819485045 (ISBN)
    Conference
    Medical imaging 2011 - Image Processing, Lake Buena Vista, Florida, USA, 14–16 February 2011
    Note

    Original Publication: Chunliang Wang, Hans Frimmel and Örjan Smedby, Level-set based vessel segmentation accelerated with periodic monotonic speed function, 2011, SPIE medical imaging 2011 Lake Buena Vista, Florida, USA. http://dx.doi.org/10.1117/12.876704 Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

    Available from: 2011-05-05 Created: 2011-05-05 Last updated: 2015-08-20Bibliographically approved
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    Computer-­Assisted Coronary CT Angiography Analysis: From Software Development to Clinical Application
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    omslag
  • 16.
    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.

  • 17.
    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). Sectra, Linkoping, Sweden.
    Forsberg, Daniel
    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). Sectra, Linkoping, Sweden.
    Segmentation of Intervertebral Discs in 3D MRI Data Using Multi-atlas Based Registration2016In: Computational Methods and Clinical Applications for Spine Imaging, CSI 2015, SPRINGER INT PUBLISHING AG , 2016, Vol. 9402, p. 107-116Conference paper (Refereed)
    Abstract [en]

    This paper presents one of the participating methods to the intervertebral disc segmentation challenge organized in conjunction with the 3rd MICCAI Workshop amp; Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI-CSI2015. The presented method consist of three steps. In the first step, vertebral bodies are detected and labeled using integral channel features and a graphical parts model. The second step consists of image registration, where a set of image volumes with corresponding intervertebral disc atlases are registered to the target volume using the output from the first step as initialization. In the final step, the registered atlases are combined using label fusion to derive the final segmentation. The pipeline was evaluated using a set of 15 + 10 T2-weighted image volumes provided as training and test data respectively for the segmentation challenge. For the training data, a mean disc centroid distance of 0.86 mm and an average DICE score of 91% was achieved, and for the test data the corresponding results were 0.90 mm and 90%.

  • 18.
    Wang, Chunliang
    et al.
    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).
    Frimmel, Hans
    Institutionen för informationteknologi, Uppsala universitet, 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, 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.
    An interactive software module for visualizing coronary arteries in CT angiography2008In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Vol. 3, no 1-2, p. 11-18Article in journal (Refereed)
    Abstract [en]

    A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT).

    Materials and Methods:  The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized “virtual contrast injection” algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3–0.5 mm.

    Results:  The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches.

    Conclusion:  The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.

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    FULLTEXT01
  • 19.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Frimmel, Hans
    Uppsala University, Sweden .
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. 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.
    Fast level-set based image segmentation using coherent propagation2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 7, article id 073501Article in journal (Refereed)
    Abstract [en]

    Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets.

    Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance.

    Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases.

    Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images. (C) 2014 American Association of Physicists in Medicine.

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    fulltext
  • 20.
    Wang, Chunliang
    et al.
    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).
    Frimmel, Hans
    Uppsala University.
    Smedby, Örjan
    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.
    Level-set based vessel segmentation accelerated with periodic monotonic speed function2011In: Medical Imaging 2011: Image Processing / [ed] Benoit M. Dawant, David R. Haynor, SPIE - International Society for Optical Engineering, 2011, Vol. 7962, p. 79621M-1-79621M-7Conference paper (Refereed)
    Abstract [en]

    To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour’s local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points’ expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.

    Download full text (pdf)
    FULLTEXT01
  • 21.
    Wang, Chunliang
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden; School of Technology and Health, Royal Institute of Technology - KTH, Stockholm, 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.
    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.

  • 22.
    Wang, Chunliang
    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.
    Moreno, Rodrigo
    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.
    Inter-project Cooperation Using Socket-Based Inter-Process Communication2012Conference paper (Other academic)
  • 23.
    Wang, Chunliang
    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.
    Moreno, Rodrigo
    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.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Vessel Segmentation Using Implicit Model-Guided Level Sets2012Conference paper (Refereed)
    Abstract [en]

    This paper proposes an automatic segmentation method of vasculature that combines level-sets with an implicit 3D model of the vessels. First, a 3D vessel model from a set of initial centerlines is generated. This model is incorporated in the level set propagation to regulate the growth of the vessel contour. After evolving the level set, new centerlines are extracted and the diameter of vessels is re-estimated in order to generate a new vessel model. The propagation and re-modeling steps are repeated until convergence. The organizers of the 3D Cardiovascular Imaging: a MICCAI segmentation challenge report the following results for the 24 testing datasets. The sensitivity and PPV are 0.26, 0.40 for QCA and 0.05 and 0.22 for CTA. As for quantitation, the absolute and RMS dierences for QCA are 29.7% and 34.1% and the weighted kappa for CTA are -0.37. As for lumen segmentation, the dice are 0.68 and 0.69 for healthy and diseased vessel segments respectively. Performance for QCA and lumen segmentation are close to the reported by the organizers for three human observers.

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    fulltext
  • 24.
    Wang, Chunliang
    et al.
    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.
    Persson, Anders
    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. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    de Geer, Jakob
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology.
    Björkholm, Anders
    Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Czekierda, Waldemar
    Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Fransson, Sven Göran
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    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.
    Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?2012In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 8, p. 845-851Article in journal (Refereed)
    Abstract [en]

    Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenoses with catheter angiography (CA) as the reference standard.

    Method: CCTA data sets from 30 patients were acquired with a 64-slice dual source CT scanner and segmented by an independent observer using the region growing (RG) method and the “virtual contrast injection” (VC) method. For every examination, each of the three types of images was  then reviewed by one of three reviewers in a blinded fashion for the presence of stenoses with diameter reduction of 50% or more. For the original series, the reviewer was allowed to use all the 2D or 3D visualization tools available (mixed method). For the segmented results (from RG and VC), the reviewer only used the 3D maximum intensity projection. Evaluation results were compared with CA for each artery.

    Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the mixed method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the mixed method (p = 0.22). Excluding vessels with heavy calcification, all three methods had similar accuracy.

    Conclusion: Diagnostic accuracy when using segmented 3D data was lower than with access to 2D images. However, the high NPV of the 3D methods suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. The VC method, which generates more evaluable arteries and has higher accuracy, seems more promising for this purpose than the RG method.

    Download full text (pdf)
    fulltext
  • 25.
    Wang, Chunliang
    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.
    Ritter, Felix
    Institute for Medical Image Computing, Bremen, Germany.
    Smedby, Orjan
    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.
    Making the PACS workstation a browser of image processing software: a feasibility study using inter-process communication techniques2010In: International journal of computer assisted radiology and surgery, ISSN 1861-6429, Vol. 5, no 4, p. 411-419Article in journal (Refereed)
    Abstract [en]

    PURPOSE: To enhance the functional expandability of a picture archiving and communication systems (PACS) workstation and to facilitate the integration of third-part image-processing modules, we propose a browser-server style method. METHODS: In the proposed solution, the PACS workstation shows the front-end user interface defined in an XML file while the image processing software is running in the background as a server. Inter-process communication (IPC) techniques allow an efficient exchange of image data, parameters, and user input between the PACS workstation and stand-alone image-processing software. Using a predefined communication protocol, the PACS workstation developer or image processing software developer does not need detailed information about the other system, but will still be able to achieve seamless integration between the two systems and the IPC procedure is totally transparent to the final user. RESULTS: A browser-server style solution was built between OsiriX (PACS workstation software) and MeVisLab (Image-Processing Software). Ten example image-processing modules were easily added to OsiriX by converting existing MeVisLab image processing networks. Image data transfer using shared memory added <10ms of processing time while the other IPC methods cost 1-5 s in our experiments. CONCLUSION: The browser-server style communication based on IPC techniques is an appealing method that allows PACS workstation developers and image processing software developers to cooperate while focusing on different interests.

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    FULLTEXT01
  • 26.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. 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 Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    An Automatic Seeding Method For Coronary Artery Segmentation and Skeletonization in CTA2008Conference paper (Other academic)
    Abstract [en]

    An automatic seeding method for coronary artery segmentation and skeletonization is presented. The new method includes automatic removal of the rib cage, tracing of the ascending aorta and initial planting of seeds for the coronary arteries. The automatic seeds are then passed on to a “virtual contrast injection” algorithm performing segmentation and skeletonization. In preliminary experiments, most main branches of the coronary tree were segmented and skeletonized without any user interaction.

  • 27.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. 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 Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors2014Conference paper (Other academic)
    Abstract [en]

    An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented rst, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coecient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.

  • 28.
    Wang, Chunliang
    et al.
    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).
    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.
    Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sébastien Ourselin, Anthony Maeder, Springer Berlin/Heidelberg, 2007, Vol. 4791, p. 311-318Conference paper (Refereed)
    Abstract [en]

    We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.

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  • 29.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. 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 Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Fully automatic brain segmentation using model-guided level set and skeleton based models2013Conference paper (Other academic)
    Abstract [en]

    A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

  • 30.
    Wang, Chunliang
    et al.
    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).
    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.
    Integrating automatic and interactive method for coronary artery segmentation: let PACS workstation think ahead2010In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 5, no 3, p. 275-285Article in journal (Refereed)
    Abstract [en]

    Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.

    Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.

    Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.

    Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.

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  • 31.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. 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 Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Model-based left ventricle segmentation in 3D ultrasound using phase image2014Conference paper (Other academic)
    Abstract [en]

    In this paper, we propose a semi-automatic method for left ventricle segmentation. The proposed method utilizes a multi-scale quadrature filter method to enhance the 3D volume, followed by a model-based level set method to segment the endocardial surface of the left ventricle. The phase map from the quadrature filters is also used to weight the influence of contour points when updating the statistical model.

  • 32.
    Zdolsek, Georg
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Chen, Yupei
    Department of Biomedical Engineering and Health Systems, Royal Institute of Technology, Sweden.
    Bögl, Hans Peter
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Department of Orthopedic Surgery, Gävle Hospital, Sweden.
    Wang, Chunliang
    Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Woisetschläger, Mischa
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Schilcher, Jörg
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures2021In: Acta Orthopaedica, ISSN 1745-3674, E-ISSN 1745-3682, Vol. 92, no 4, p. 394-400Article in journal (Refereed)
    Abstract [en]

    Background and purpose - A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagnostic accuracy of radiographs with standard radiology reports is very poor. We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs. Patients and methods - We entered 433 radiographs from 149 patients with complete AFF and 549 radiographs from 224 patients with NFF into a convolutional neural network (CNN) that acts as a core classifier in an automated pathway and a manual intervention pathway (manual improvement of image orientation). We tested several deep neural network structures (i.e., VGG19, InceptionV3, and ResNet) to identify the network with the highest diagnostic accuracy for distinguishing AFF from NFF. We applied a transfer learning technique and used 5-fold cross-validation and class activation mapping to evaluate the diagnostic accuracy.Results - In the automated pathway, ResNet50 had the highest diagnostic accuracy, with a mean of 91% (SD 1.3), as compared with 83% (SD 1.6) for VGG19, and 89% (SD 2.5) for InceptionV3. The corresponding accuracy levels for the intervention pathway were 94% (SD 2.0), 92% (2.7), and 93% (3.7), respectively. With regards to sensitivity and specificity, ResNet outperformed the other networks with a mean AUC (area under the curve) value of 0.94 (SD 0.01) and surpassed the accuracy of clinical diagnostics.Interpretation - Artificial intelligence systems show excellent diagnostic accuracies for the rare fracture type of AFF in an experimental setting.

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