Right ventricle segmentation from cardiac MRI: a collation study
Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Petitjean, Caroline [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Rechteinformationen: |
Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. |
|---|
| Schlagwörter: |
Ventricular Dysfunction, Left - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods |
|---|
| Übergeordnetes Werk: |
Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier, 1996, 19(2015), 1, Seite 187-202 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2015 ; number:1 ; pages:187-202 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.media.2014.10.004 |
|---|
| Katalog-ID: |
OLC1960853139 |
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| 520 | |a Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). | ||
| 540 | |a Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. | ||
| 650 | 4 | |a Ventricular Dysfunction, Left - pathology | |
| 650 | 4 | |a Heart Ventricles - pathology | |
| 650 | 4 | |a Image Interpretation, Computer-Assisted - methods | |
| 650 | 4 | |a Imaging, Three-Dimensional - methods | |
| 650 | 4 | |a Magnetic Resonance Imaging, Cine - methods | |
| 650 | 4 | |a Pattern Recognition, Automated - methods | |
| 650 | 4 | |a Image Enhancement - methods | |
| 700 | 1 | |a Zuluaga, Maria A |4 oth | |
| 700 | 1 | |a Bai, Wenjia |4 oth | |
| 700 | 1 | |a Dacher, Jean-Nicolas |4 oth | |
| 700 | 1 | |a Grosgeorge, Damien |4 oth | |
| 700 | 1 | |a Caudron, Jérôme |4 oth | |
| 700 | 1 | |a Ruan, Su |4 oth | |
| 700 | 1 | |a Ayed, Ismail Ben |4 oth | |
| 700 | 1 | |a Cardoso, M Jorge |4 oth | |
| 700 | 1 | |a Chen, Hsiang-Chou |4 oth | |
| 700 | 1 | |a Jimenez-Carretero, Daniel |4 oth | |
| 700 | 1 | |a Ledesma-Carbayo, Maria J |4 oth | |
| 700 | 1 | |a Davatzikos, Christos |4 oth | |
| 700 | 1 | |a Doshi, Jimit |4 oth | |
| 700 | 1 | |a Erus, Guray |4 oth | |
| 700 | 1 | |a Maier, Oskar M O |4 oth | |
| 700 | 1 | |a Nambakhsh, Cyrus M S |4 oth | |
| 700 | 1 | |a Ou, Yangming |4 oth | |
| 700 | 1 | |a Ourselin, Sébastien |4 oth | |
| 700 | 1 | |a Peng, Chun-Wei |4 oth | |
| 700 | 1 | |a Peters, Nicholas S |4 oth | |
| 700 | 1 | |a Peters, Terry M |4 oth | |
| 700 | 1 | |a Rajchl, Martin |4 oth | |
| 700 | 1 | |a Rueckert, Daniel |4 oth | |
| 700 | 1 | |a Santos, Andres |4 oth | |
| 700 | 1 | |a Shi, Wenzhe |4 oth | |
| 700 | 1 | |a Wang, Ching-Wei |4 oth | |
| 700 | 1 | |a Wang, Haiyan |4 oth | |
| 700 | 1 | |a Yuan, Jing |4 oth | |
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10.1016/j.media.2014.10.004 doi PQ20160617 (DE-627)OLC1960853139 (DE-599)GBVOLC1960853139 (PRQ)c1275-e65d1d25b7f2d8781d218631f7c858deb8e9f4088dbcbe4f4a712776623f53940 (KEY)0392983320150000019000100187rightventriclesegmentationfromcardiacmriacollation DE-627 ger DE-627 rakwb eng 004 ZDB Petitjean, Caroline verfasserin aut Right ventricle segmentation from cardiac MRI: a collation study 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Ventricular Dysfunction, Left - pathology Heart Ventricles - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods Pattern Recognition, Automated - methods Image Enhancement - methods Zuluaga, Maria A oth Bai, Wenjia oth Dacher, Jean-Nicolas oth Grosgeorge, Damien oth Caudron, Jérôme oth Ruan, Su oth Ayed, Ismail Ben oth Cardoso, M Jorge oth Chen, Hsiang-Chou oth Jimenez-Carretero, Daniel oth Ledesma-Carbayo, Maria J oth Davatzikos, Christos oth Doshi, Jimit oth Erus, Guray oth Maier, Oskar M O oth Nambakhsh, Cyrus M S oth Ou, Yangming oth Ourselin, Sébastien oth Peng, Chun-Wei oth Peters, Nicholas S oth Peters, Terry M oth Rajchl, Martin oth Rueckert, Daniel oth Santos, Andres oth Shi, Wenzhe oth Wang, Ching-Wei oth Wang, Haiyan oth Yuan, Jing oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 19(2015), 1, Seite 187-202 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:19 year:2015 number:1 pages:187-202 http://dx.doi.org/10.1016/j.media.2014.10.004 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25461337 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 19 2015 1 187-202 |
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10.1016/j.media.2014.10.004 doi PQ20160617 (DE-627)OLC1960853139 (DE-599)GBVOLC1960853139 (PRQ)c1275-e65d1d25b7f2d8781d218631f7c858deb8e9f4088dbcbe4f4a712776623f53940 (KEY)0392983320150000019000100187rightventriclesegmentationfromcardiacmriacollation DE-627 ger DE-627 rakwb eng 004 ZDB Petitjean, Caroline verfasserin aut Right ventricle segmentation from cardiac MRI: a collation study 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Ventricular Dysfunction, Left - pathology Heart Ventricles - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods Pattern Recognition, Automated - methods Image Enhancement - methods Zuluaga, Maria A oth Bai, Wenjia oth Dacher, Jean-Nicolas oth Grosgeorge, Damien oth Caudron, Jérôme oth Ruan, Su oth Ayed, Ismail Ben oth Cardoso, M Jorge oth Chen, Hsiang-Chou oth Jimenez-Carretero, Daniel oth Ledesma-Carbayo, Maria J oth Davatzikos, Christos oth Doshi, Jimit oth Erus, Guray oth Maier, Oskar M O oth Nambakhsh, Cyrus M S oth Ou, Yangming oth Ourselin, Sébastien oth Peng, Chun-Wei oth Peters, Nicholas S oth Peters, Terry M oth Rajchl, Martin oth Rueckert, Daniel oth Santos, Andres oth Shi, Wenzhe oth Wang, Ching-Wei oth Wang, Haiyan oth Yuan, Jing oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 19(2015), 1, Seite 187-202 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:19 year:2015 number:1 pages:187-202 http://dx.doi.org/10.1016/j.media.2014.10.004 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25461337 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 19 2015 1 187-202 |
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10.1016/j.media.2014.10.004 doi PQ20160617 (DE-627)OLC1960853139 (DE-599)GBVOLC1960853139 (PRQ)c1275-e65d1d25b7f2d8781d218631f7c858deb8e9f4088dbcbe4f4a712776623f53940 (KEY)0392983320150000019000100187rightventriclesegmentationfromcardiacmriacollation DE-627 ger DE-627 rakwb eng 004 ZDB Petitjean, Caroline verfasserin aut Right ventricle segmentation from cardiac MRI: a collation study 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Ventricular Dysfunction, Left - pathology Heart Ventricles - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods Pattern Recognition, Automated - methods Image Enhancement - methods Zuluaga, Maria A oth Bai, Wenjia oth Dacher, Jean-Nicolas oth Grosgeorge, Damien oth Caudron, Jérôme oth Ruan, Su oth Ayed, Ismail Ben oth Cardoso, M Jorge oth Chen, Hsiang-Chou oth Jimenez-Carretero, Daniel oth Ledesma-Carbayo, Maria J oth Davatzikos, Christos oth Doshi, Jimit oth Erus, Guray oth Maier, Oskar M O oth Nambakhsh, Cyrus M S oth Ou, Yangming oth Ourselin, Sébastien oth Peng, Chun-Wei oth Peters, Nicholas S oth Peters, Terry M oth Rajchl, Martin oth Rueckert, Daniel oth Santos, Andres oth Shi, Wenzhe oth Wang, Ching-Wei oth Wang, Haiyan oth Yuan, Jing oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 19(2015), 1, Seite 187-202 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:19 year:2015 number:1 pages:187-202 http://dx.doi.org/10.1016/j.media.2014.10.004 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25461337 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 19 2015 1 187-202 |
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10.1016/j.media.2014.10.004 doi PQ20160617 (DE-627)OLC1960853139 (DE-599)GBVOLC1960853139 (PRQ)c1275-e65d1d25b7f2d8781d218631f7c858deb8e9f4088dbcbe4f4a712776623f53940 (KEY)0392983320150000019000100187rightventriclesegmentationfromcardiacmriacollation DE-627 ger DE-627 rakwb eng 004 ZDB Petitjean, Caroline verfasserin aut Right ventricle segmentation from cardiac MRI: a collation study 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Ventricular Dysfunction, Left - pathology Heart Ventricles - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods Pattern Recognition, Automated - methods Image Enhancement - methods Zuluaga, Maria A oth Bai, Wenjia oth Dacher, Jean-Nicolas oth Grosgeorge, Damien oth Caudron, Jérôme oth Ruan, Su oth Ayed, Ismail Ben oth Cardoso, M Jorge oth Chen, Hsiang-Chou oth Jimenez-Carretero, Daniel oth Ledesma-Carbayo, Maria J oth Davatzikos, Christos oth Doshi, Jimit oth Erus, Guray oth Maier, Oskar M O oth Nambakhsh, Cyrus M S oth Ou, Yangming oth Ourselin, Sébastien oth Peng, Chun-Wei oth Peters, Nicholas S oth Peters, Terry M oth Rajchl, Martin oth Rueckert, Daniel oth Santos, Andres oth Shi, Wenzhe oth Wang, Ching-Wei oth Wang, Haiyan oth Yuan, Jing oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 19(2015), 1, Seite 187-202 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:19 year:2015 number:1 pages:187-202 http://dx.doi.org/10.1016/j.media.2014.10.004 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25461337 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 19 2015 1 187-202 |
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10.1016/j.media.2014.10.004 doi PQ20160617 (DE-627)OLC1960853139 (DE-599)GBVOLC1960853139 (PRQ)c1275-e65d1d25b7f2d8781d218631f7c858deb8e9f4088dbcbe4f4a712776623f53940 (KEY)0392983320150000019000100187rightventriclesegmentationfromcardiacmriacollation DE-627 ger DE-627 rakwb eng 004 ZDB Petitjean, Caroline verfasserin aut Right ventricle segmentation from cardiac MRI: a collation study 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Ventricular Dysfunction, Left - pathology Heart Ventricles - pathology Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Magnetic Resonance Imaging, Cine - methods Pattern Recognition, Automated - methods Image Enhancement - methods Zuluaga, Maria A oth Bai, Wenjia oth Dacher, Jean-Nicolas oth Grosgeorge, Damien oth Caudron, Jérôme oth Ruan, Su oth Ayed, Ismail Ben oth Cardoso, M Jorge oth Chen, Hsiang-Chou oth Jimenez-Carretero, Daniel oth Ledesma-Carbayo, Maria J oth Davatzikos, Christos oth Doshi, Jimit oth Erus, Guray oth Maier, Oskar M O oth Nambakhsh, Cyrus M S oth Ou, Yangming oth Ourselin, Sébastien oth Peng, Chun-Wei oth Peters, Nicholas S oth Peters, Terry M oth Rajchl, Martin oth Rueckert, Daniel oth Santos, Andres oth Shi, Wenzhe oth Wang, Ching-Wei oth Wang, Haiyan oth Yuan, Jing oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 19(2015), 1, Seite 187-202 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:19 year:2015 number:1 pages:187-202 http://dx.doi.org/10.1016/j.media.2014.10.004 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25461337 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 19 2015 1 187-202 |
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Petitjean, Caroline @@aut@@ Zuluaga, Maria A @@oth@@ Bai, Wenjia @@oth@@ Dacher, Jean-Nicolas @@oth@@ Grosgeorge, Damien @@oth@@ Caudron, Jérôme @@oth@@ Ruan, Su @@oth@@ Ayed, Ismail Ben @@oth@@ Cardoso, M Jorge @@oth@@ Chen, Hsiang-Chou @@oth@@ Jimenez-Carretero, Daniel @@oth@@ Ledesma-Carbayo, Maria J @@oth@@ Davatzikos, Christos @@oth@@ Doshi, Jimit @@oth@@ Erus, Guray @@oth@@ Maier, Oskar M O @@oth@@ Nambakhsh, Cyrus M S @@oth@@ Ou, Yangming @@oth@@ Ourselin, Sébastien @@oth@@ Peng, Chun-Wei @@oth@@ Peters, Nicholas S @@oth@@ Peters, Terry M @@oth@@ Rajchl, Martin @@oth@@ Rueckert, Daniel @@oth@@ Santos, Andres @@oth@@ Shi, Wenzhe @@oth@@ Wang, Ching-Wei @@oth@@ Wang, Haiyan @@oth@@ Yuan, Jing @@oth@@ |
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Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). |
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Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). |
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Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/). |
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Zuluaga, Maria A Bai, Wenjia Dacher, Jean-Nicolas Grosgeorge, Damien Caudron, Jérôme Ruan, Su Ayed, Ismail Ben Cardoso, M Jorge Chen, Hsiang-Chou Jimenez-Carretero, Daniel Ledesma-Carbayo, Maria J Davatzikos, Christos Doshi, Jimit Erus, Guray Maier, Oskar M O Nambakhsh, Cyrus M S Ou, Yangming Ourselin, Sébastien Peng, Chun-Wei Peters, Nicholas S Peters, Terry M Rajchl, Martin Rueckert, Daniel Santos, Andres Shi, Wenzhe Wang, Ching-Wei Wang, Haiyan Yuan, Jing |
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Zuluaga, Maria A Bai, Wenjia Dacher, Jean-Nicolas Grosgeorge, Damien Caudron, Jérôme Ruan, Su Ayed, Ismail Ben Cardoso, M Jorge Chen, Hsiang-Chou Jimenez-Carretero, Daniel Ledesma-Carbayo, Maria J Davatzikos, Christos Doshi, Jimit Erus, Guray Maier, Oskar M O Nambakhsh, Cyrus M S Ou, Yangming Ourselin, Sébastien Peng, Chun-Wei Peters, Nicholas S Peters, Terry M Rajchl, Martin Rueckert, Daniel Santos, Andres Shi, Wenzhe Wang, Ching-Wei Wang, Haiyan Yuan, Jing |
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