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Palm V, Norajitra T, von Stackelberg O, Heussel CP, Skornitzke S, Weinheimer O, Kopytova T, Klein A, Almeida SD, Baumgartner M, Bounias D, Scherer J, Kades K, Gao H, Jäger P, Nolden M, Tong E, Eckl K, Nattenmüller J, Nonnenmacher T, Naas O, Reuter J, Bischoff A, Kroschke J, Rengier F, Schlamp K, Debic M, Kauczor HU, Maier-Hein K, Wielpütz MO. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine. Healthcare (Basel) 2022; 10:2166. [PMID: 36360507 PMCID: PMC9690402 DOI: 10.3390/healthcare10112166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 08/12/2023] Open
Abstract
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.
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Affiliation(s)
- Viktoria Palm
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Tobias Norajitra
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Claus P. Heussel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Stephan Skornitzke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Taisiya Kopytova
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Andre Klein
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Silvia D. Almeida
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Dimitrios Bounias
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Klaus Kades
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Hanno Gao
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Paul Jäger
- Interactive Machine Learning Research Group, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Elizabeth Tong
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kira Eckl
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Omar Naas
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Julia Reuter
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Arved Bischoff
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Jonas Kroschke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Fabian Rengier
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Manuel Debic
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Klaus Maier-Hein
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
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Gogin N, Viti M, Nicodème L, Ohana M, Talbot H, Gencer U, Mekukosokeng M, Caramella T, Diascorn Y, Airaud JY, Guillot MS, Bensalah Z, Dam Hieu C, Abdallah B, Bousaid I, Lassau N, Mousseaux E. Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning. Diagn Interv Imaging 2021; 102:683-690. [PMID: 34099435 DOI: 10.1016/j.diii.2021.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN). MATERIALS AND METHODS The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. RESULTS The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. CONCLUSION The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.
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Affiliation(s)
| | - Mario Viti
- General Electric Healthcare, 78530 Buc, France; CentraleSupélec, Université Paris-Saclay, CentraleSupélec, Inria, 91192 Gif-sur-Yvette, France
| | | | - Mickaël Ohana
- Service de Radiologie, CHU de Strasbourg, 67000 Strasbourg, France
| | - Hugues Talbot
- CentraleSupélec, Université Paris-Saclay, CentraleSupélec, Inria, 91192 Gif-sur-Yvette, France
| | - Umit Gencer
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
| | | | | | - Yann Diascorn
- Institut Arnault Tzanck, 06123 Saint-Laurent-du-Var, France
| | - Jean-Yves Airaud
- Department of Radiology, Polyclinique Inkermann, 79000 Niort, France
| | - Marc-Samir Guillot
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
| | - Zoubir Bensalah
- Department of Radiology, Centre Hospitalier de Perpignan, 66000 Perpignan, France
| | | | | | - Imad Bousaid
- Imaging Department, Gustave-Roussy, Université Paris-Saclay, 94076 Villejuif, France
| | - Nathalie Lassau
- Imaging Department, Gustave-Roussy, Université Paris-Saclay, 94076 Villejuif, France; Biomaps, UMR 1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94076 Villejuif, France
| | - Elie Mousseaux
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
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Sandstedt M, Henriksson L, Janzon M, Nyberg G, Engvall J, De Geer J, Alfredsson J, Persson A. Evaluation of an AI-based, automatic coronary artery calcium scoring software. Eur Radiol 2019; 30:1671-1678. [PMID: 31728692 PMCID: PMC7033052 DOI: 10.1007/s00330-019-06489-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/26/2019] [Accepted: 10/09/2019] [Indexed: 11/04/2022]
Abstract
Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.
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Affiliation(s)
- Mårten Sandstedt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. .,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden.
| | - Lilian Henriksson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Magnus Janzon
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Gusten Nyberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Jakob De Geer
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Joakim Alfredsson
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Anders Persson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
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