Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge
Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment...
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| Published in | Journal of cardiovascular magnetic resonance Vol. 15; no. 1; p. 105 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Elsevier Inc
20.12.2013
BioMed Central BioMed Central Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1097-6647 1532-429X 1532-429X |
| DOI | 10.1186/1532-429X-15-105 |
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| Abstract | Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.
The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.
Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.
The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. |
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| AbstractList | Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.
The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.
Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.
The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Keywords: Late gadolinium enhancement, Cardiovascular magnetic resonance, Atrial fibrillation, Segmentation, Algorithm benchmarking Doc number: 105 Abstract Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods: The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results: Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions: The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods: The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results: Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions: The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.BACKGROUNDLate Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.METHODSThe image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.RESULTSSome algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.CONCLUSIONSThe study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface. |
| ArticleNumber | 105 |
| Audience | Academic |
| Author | Gao, Yi Lu, YingLi Perry, Daniel Acheampong, Prince Karim, Rashed Hennemuth, Anja Radau, Perry Schaeffter, Tobias Tannenbaum, Allen Razavi, Reza Housden, R James Rhode, Kawal Uddin, Ayesha MacLeod, Rob Balasubramaniam, Mayuragoban Bai, Wenjia Obom, Samantha Palkhi, Ebrahim Al-Beyatti, Yosra Peters, Dana Chen, Zhong Peitgen, Heinz-Otto Rueckert, Daniel Cates, Josh Shi, Wenzhe |
| AuthorAffiliation | 1 Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK 8 Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany 2 Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA 4 Department of Computing, Imperial College London, London, UK 7 Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada 5 School of Electrical and Computer Engineering, Boston University, Boston, USA 3 Magnetic Resonance Research Centre, Yale School of Medicine, Yale University, New Haven, USA 9 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA 6 Psychiatry Neuroimaging Lab, Harvard Medical School, Boston, USA |
| AuthorAffiliation_xml | – name: 7 Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada – name: 8 Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany – name: 6 Psychiatry Neuroimaging Lab, Harvard Medical School, Boston, USA – name: 4 Department of Computing, Imperial College London, London, UK – name: 2 Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA – name: 3 Magnetic Resonance Research Centre, Yale School of Medicine, Yale University, New Haven, USA – name: 5 School of Electrical and Computer Engineering, Boston University, Boston, USA – name: 9 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA – name: 1 Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK |
| Author_xml | – sequence: 1 givenname: Rashed surname: Karim fullname: Karim, Rashed email: rashed.karim@kcl.ac.uk organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 2 givenname: R James surname: Housden fullname: Housden, R James organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 3 givenname: Mayuragoban surname: Balasubramaniam fullname: Balasubramaniam, Mayuragoban organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 4 givenname: Zhong surname: Chen fullname: Chen, Zhong organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 5 givenname: Daniel surname: Perry fullname: Perry, Daniel organization: Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA – sequence: 6 givenname: Ayesha surname: Uddin fullname: Uddin, Ayesha organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 7 givenname: Yosra surname: Al-Beyatti fullname: Al-Beyatti, Yosra organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 8 givenname: Ebrahim surname: Palkhi fullname: Palkhi, Ebrahim organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 9 givenname: Prince surname: Acheampong fullname: Acheampong, Prince organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 10 givenname: Samantha surname: Obom fullname: Obom, Samantha organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 11 givenname: Anja surname: Hennemuth fullname: Hennemuth, Anja organization: Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany – sequence: 12 givenname: YingLi surname: Lu fullname: Lu, YingLi organization: Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada – sequence: 13 givenname: Wenjia surname: Bai fullname: Bai, Wenjia organization: Department of Computing, Imperial College London, London, UK – sequence: 14 givenname: Wenzhe surname: Shi fullname: Shi, Wenzhe organization: Department of Computing, Imperial College London, London, UK – sequence: 15 givenname: Yi surname: Gao fullname: Gao, Yi organization: Psychiatry Neuroimaging Lab, Harvard Medical School, Boston, USA – sequence: 16 givenname: Heinz-Otto surname: Peitgen fullname: Peitgen, Heinz-Otto organization: Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany – sequence: 17 givenname: Perry surname: Radau fullname: Radau, Perry organization: Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada – sequence: 18 givenname: Reza surname: Razavi fullname: Razavi, Reza organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 19 givenname: Allen surname: Tannenbaum fullname: Tannenbaum, Allen organization: School of Electrical and Computer Engineering, Boston University, Boston, USA – sequence: 20 givenname: Daniel surname: Rueckert fullname: Rueckert, Daniel organization: Department of Computing, Imperial College London, London, UK – sequence: 21 givenname: Josh surname: Cates fullname: Cates, Josh organization: Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA – sequence: 22 givenname: Tobias surname: Schaeffter fullname: Schaeffter, Tobias organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK – sequence: 23 givenname: Dana surname: Peters fullname: Peters, Dana organization: Magnetic Resonance Research Centre, Yale School of Medicine, Yale University, New Haven, USA – sequence: 24 givenname: Rob surname: MacLeod fullname: MacLeod, Rob organization: Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA – sequence: 25 givenname: Kawal surname: Rhode fullname: Rhode, Kawal organization: Department of Imaging Sciences & Biomedical Engineering, King's College London, London, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24359544$$D View this record in MEDLINE/PubMed |
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| Copyright | 2013 THE AUTHORS. Published by Elsevier Inc on behalf of the Society for Cardiovascular Magnetic Resonance Karim et al.; licensee BioMed Central Ltd. 2013 COPYRIGHT 2013 BioMed Central Ltd. 2013 Karim et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2013 Karim et al.; licensee BioMed Central Ltd. 2013 Karim et al.; licensee BioMed Central Ltd. |
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| Snippet | Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium... Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the... Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the... Doc number: 105 Abstract Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of... Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the... |
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| StartPage | 105 |
| SubjectTerms | Algorithm benchmarking Algorithms Angiology Atrial fibrillation Atrial Fibrillation - diagnosis Atrial Fibrillation - pathology Benchmarking Biomedical research Cardiology Cardiovascular magnetic resonance Care and treatment Cicatrix - diagnosis Cicatrix - pathology Colleges & universities Contrast Media Databases, Factual Diagnosis Europe Evaluation Fibrosis Gadolinium Health aspects Heart Atria - pathology Heart diseases Humans Image Interpretation, Computer-Assisted - standards Imaging Late gadolinium enhancement Magnetic resonance imaging Magnetic Resonance Imaging - standards Medical research Medicine Medicine & Public Health Methods Observer Variation Predictive Value of Tests Radiology Reproducibility of Results Segmentation United States |
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| Title | Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge |
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