ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Maier, Oskar [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Übergeordnetes Werk: |
Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier, 1996, 35(2017), Seite 250-269 |
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| Übergeordnetes Werk: |
volume:35 ; year:2017 ; pages:250-269 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.media.2016.07.009 |
|---|
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OLC1989760058 |
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| 520 | |a Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). | ||
| 700 | 1 | |a Menze, Bjoern H |4 oth | |
| 700 | 1 | |a von der Gablentz, Janina |4 oth | |
| 700 | 1 | |a Häni, Levin |4 oth | |
| 700 | 1 | |a Heinrich, Mattias P |4 oth | |
| 700 | 1 | |a Liebrand, Matthias |4 oth | |
| 700 | 1 | |a Winzeck, Stefan |4 oth | |
| 700 | 1 | |a Basit, Abdul |4 oth | |
| 700 | 1 | |a Bentley, Paul |4 oth | |
| 700 | 1 | |a Chen, Liang |4 oth | |
| 700 | 1 | |a Christiaens, Daan |4 oth | |
| 700 | 1 | |a Dutil, Francis |4 oth | |
| 700 | 1 | |a Egger, Karl |4 oth | |
| 700 | 1 | |a Feng, Chaolu |4 oth | |
| 700 | 1 | |a Glocker, Ben |4 oth | |
| 700 | 1 | |a Götz, Michael |4 oth | |
| 700 | 1 | |a Haeck, Tom |4 oth | |
| 700 | 1 | |a Halme, Hanna-Leena |4 oth | |
| 700 | 1 | |a Havaei, Mohammad |4 oth | |
| 700 | 1 | |a Iftekharuddin, Khan M |4 oth | |
| 700 | 1 | |a Jodoin, Pierre-Marc |4 oth | |
| 700 | 1 | |a Kamnitsas, Konstantinos |4 oth | |
| 700 | 1 | |a Kellner, Elias |4 oth | |
| 700 | 1 | |a Korvenoja, Antti |4 oth | |
| 700 | 1 | |a Larochelle, Hugo |4 oth | |
| 700 | 1 | |a Ledig, Christian |4 oth | |
| 700 | 1 | |a Lee, Jia-Hong |4 oth | |
| 700 | 1 | |a Maes, Frederik |4 oth | |
| 700 | 1 | |a Mahmood, Qaiser |4 oth | |
| 700 | 1 | |a Maier-Hein, Klaus H |4 oth | |
| 700 | 1 | |a McKinley, Richard |4 oth | |
| 700 | 1 | |a Muschelli, John |4 oth | |
| 700 | 1 | |a Pal, Chris |4 oth | |
| 700 | 1 | |a Pei, Linmin |4 oth | |
| 700 | 1 | |a Rangarajan, Janaki Raman |4 oth | |
| 700 | 1 | |a Reza, Syed M.S |4 oth | |
| 700 | 1 | |a Robben, David |4 oth | |
| 700 | 1 | |a Rueckert, Daniel |4 oth | |
| 700 | 1 | |a Salli, Eero |4 oth | |
| 700 | 1 | |a Suetens, Paul |4 oth | |
| 700 | 1 | |a Wang, Ching-Wei |4 oth | |
| 700 | 1 | |a Wilms, Matthias |4 oth | |
| 700 | 1 | |a Kirschke, Jan S |4 oth | |
| 700 | 1 | |a Krämer, Ulrike M |4 oth | |
| 700 | 1 | |a Münte, Thomas F |4 oth | |
| 700 | 1 | |a Schramm, Peter |4 oth | |
| 700 | 1 | |a Wiest, Roland |4 oth | |
| 700 | 1 | |a Handels, Heinz |4 oth | |
| 700 | 1 | |a Reyes, Mauricio |4 oth | |
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10.1016/j.media.2016.07.009 doi PQ20170501 (DE-627)OLC1989760058 (DE-599)GBVOLC1989760058 (PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0 (KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro DE-627 ger DE-627 rakwb eng 004 ZDB Maier, Oskar verfasserin aut ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). Menze, Bjoern H oth von der Gablentz, Janina oth Häni, Levin oth Heinrich, Mattias P oth Liebrand, Matthias oth Winzeck, Stefan oth Basit, Abdul oth Bentley, Paul oth Chen, Liang oth Christiaens, Daan oth Dutil, Francis oth Egger, Karl oth Feng, Chaolu oth Glocker, Ben oth Götz, Michael oth Haeck, Tom oth Halme, Hanna-Leena oth Havaei, Mohammad oth Iftekharuddin, Khan M oth Jodoin, Pierre-Marc oth Kamnitsas, Konstantinos oth Kellner, Elias oth Korvenoja, Antti oth Larochelle, Hugo oth Ledig, Christian oth Lee, Jia-Hong oth Maes, Frederik oth Mahmood, Qaiser oth Maier-Hein, Klaus H oth McKinley, Richard oth Muschelli, John oth Pal, Chris oth Pei, Linmin oth Rangarajan, Janaki Raman oth Reza, Syed M.S oth Robben, David oth Rueckert, Daniel oth Salli, Eero oth Suetens, Paul oth Wang, Ching-Wei oth Wilms, Matthias oth Kirschke, Jan S oth Krämer, Ulrike M oth Münte, Thomas F oth Schramm, Peter oth Wiest, Roland oth Handels, Heinz oth Reyes, Mauricio oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 35(2017), Seite 250-269 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:35 year:2017 pages:250-269 http://dx.doi.org/10.1016/j.media.2016.07.009 Volltext http://publications.lib.chalmers.se/publication/246821-isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation-from-multispectral GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 35 2017 250-269 |
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10.1016/j.media.2016.07.009 doi PQ20170501 (DE-627)OLC1989760058 (DE-599)GBVOLC1989760058 (PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0 (KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro DE-627 ger DE-627 rakwb eng 004 ZDB Maier, Oskar verfasserin aut ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). Menze, Bjoern H oth von der Gablentz, Janina oth Häni, Levin oth Heinrich, Mattias P oth Liebrand, Matthias oth Winzeck, Stefan oth Basit, Abdul oth Bentley, Paul oth Chen, Liang oth Christiaens, Daan oth Dutil, Francis oth Egger, Karl oth Feng, Chaolu oth Glocker, Ben oth Götz, Michael oth Haeck, Tom oth Halme, Hanna-Leena oth Havaei, Mohammad oth Iftekharuddin, Khan M oth Jodoin, Pierre-Marc oth Kamnitsas, Konstantinos oth Kellner, Elias oth Korvenoja, Antti oth Larochelle, Hugo oth Ledig, Christian oth Lee, Jia-Hong oth Maes, Frederik oth Mahmood, Qaiser oth Maier-Hein, Klaus H oth McKinley, Richard oth Muschelli, John oth Pal, Chris oth Pei, Linmin oth Rangarajan, Janaki Raman oth Reza, Syed M.S oth Robben, David oth Rueckert, Daniel oth Salli, Eero oth Suetens, Paul oth Wang, Ching-Wei oth Wilms, Matthias oth Kirschke, Jan S oth Krämer, Ulrike M oth Münte, Thomas F oth Schramm, Peter oth Wiest, Roland oth Handels, Heinz oth Reyes, Mauricio oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 35(2017), Seite 250-269 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:35 year:2017 pages:250-269 http://dx.doi.org/10.1016/j.media.2016.07.009 Volltext http://publications.lib.chalmers.se/publication/246821-isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation-from-multispectral GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 35 2017 250-269 |
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10.1016/j.media.2016.07.009 doi PQ20170501 (DE-627)OLC1989760058 (DE-599)GBVOLC1989760058 (PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0 (KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro DE-627 ger DE-627 rakwb eng 004 ZDB Maier, Oskar verfasserin aut ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). Menze, Bjoern H oth von der Gablentz, Janina oth Häni, Levin oth Heinrich, Mattias P oth Liebrand, Matthias oth Winzeck, Stefan oth Basit, Abdul oth Bentley, Paul oth Chen, Liang oth Christiaens, Daan oth Dutil, Francis oth Egger, Karl oth Feng, Chaolu oth Glocker, Ben oth Götz, Michael oth Haeck, Tom oth Halme, Hanna-Leena oth Havaei, Mohammad oth Iftekharuddin, Khan M oth Jodoin, Pierre-Marc oth Kamnitsas, Konstantinos oth Kellner, Elias oth Korvenoja, Antti oth Larochelle, Hugo oth Ledig, Christian oth Lee, Jia-Hong oth Maes, Frederik oth Mahmood, Qaiser oth Maier-Hein, Klaus H oth McKinley, Richard oth Muschelli, John oth Pal, Chris oth Pei, Linmin oth Rangarajan, Janaki Raman oth Reza, Syed M.S oth Robben, David oth Rueckert, Daniel oth Salli, Eero oth Suetens, Paul oth Wang, Ching-Wei oth Wilms, Matthias oth Kirschke, Jan S oth Krämer, Ulrike M oth Münte, Thomas F oth Schramm, Peter oth Wiest, Roland oth Handels, Heinz oth Reyes, Mauricio oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 35(2017), Seite 250-269 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:35 year:2017 pages:250-269 http://dx.doi.org/10.1016/j.media.2016.07.009 Volltext http://publications.lib.chalmers.se/publication/246821-isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation-from-multispectral GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 35 2017 250-269 |
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10.1016/j.media.2016.07.009 doi PQ20170501 (DE-627)OLC1989760058 (DE-599)GBVOLC1989760058 (PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0 (KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro DE-627 ger DE-627 rakwb eng 004 ZDB Maier, Oskar verfasserin aut ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). Menze, Bjoern H oth von der Gablentz, Janina oth Häni, Levin oth Heinrich, Mattias P oth Liebrand, Matthias oth Winzeck, Stefan oth Basit, Abdul oth Bentley, Paul oth Chen, Liang oth Christiaens, Daan oth Dutil, Francis oth Egger, Karl oth Feng, Chaolu oth Glocker, Ben oth Götz, Michael oth Haeck, Tom oth Halme, Hanna-Leena oth Havaei, Mohammad oth Iftekharuddin, Khan M oth Jodoin, Pierre-Marc oth Kamnitsas, Konstantinos oth Kellner, Elias oth Korvenoja, Antti oth Larochelle, Hugo oth Ledig, Christian oth Lee, Jia-Hong oth Maes, Frederik oth Mahmood, Qaiser oth Maier-Hein, Klaus H oth McKinley, Richard oth Muschelli, John oth Pal, Chris oth Pei, Linmin oth Rangarajan, Janaki Raman oth Reza, Syed M.S oth Robben, David oth Rueckert, Daniel oth Salli, Eero oth Suetens, Paul oth Wang, Ching-Wei oth Wilms, Matthias oth Kirschke, Jan S oth Krämer, Ulrike M oth Münte, Thomas F oth Schramm, Peter oth Wiest, Roland oth Handels, Heinz oth Reyes, Mauricio oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 35(2017), Seite 250-269 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:35 year:2017 pages:250-269 http://dx.doi.org/10.1016/j.media.2016.07.009 Volltext http://publications.lib.chalmers.se/publication/246821-isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation-from-multispectral GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 35 2017 250-269 |
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10.1016/j.media.2016.07.009 doi PQ20170501 (DE-627)OLC1989760058 (DE-599)GBVOLC1989760058 (PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0 (KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro DE-627 ger DE-627 rakwb eng 004 ZDB Maier, Oskar verfasserin aut ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). Menze, Bjoern H oth von der Gablentz, Janina oth Häni, Levin oth Heinrich, Mattias P oth Liebrand, Matthias oth Winzeck, Stefan oth Basit, Abdul oth Bentley, Paul oth Chen, Liang oth Christiaens, Daan oth Dutil, Francis oth Egger, Karl oth Feng, Chaolu oth Glocker, Ben oth Götz, Michael oth Haeck, Tom oth Halme, Hanna-Leena oth Havaei, Mohammad oth Iftekharuddin, Khan M oth Jodoin, Pierre-Marc oth Kamnitsas, Konstantinos oth Kellner, Elias oth Korvenoja, Antti oth Larochelle, Hugo oth Ledig, Christian oth Lee, Jia-Hong oth Maes, Frederik oth Mahmood, Qaiser oth Maier-Hein, Klaus H oth McKinley, Richard oth Muschelli, John oth Pal, Chris oth Pei, Linmin oth Rangarajan, Janaki Raman oth Reza, Syed M.S oth Robben, David oth Rueckert, Daniel oth Salli, Eero oth Suetens, Paul oth Wang, Ching-Wei oth Wilms, Matthias oth Kirschke, Jan S oth Krämer, Ulrike M oth Münte, Thomas F oth Schramm, Peter oth Wiest, Roland oth Handels, Heinz oth Reyes, Mauricio oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 35(2017), Seite 250-269 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:35 year:2017 pages:250-269 http://dx.doi.org/10.1016/j.media.2016.07.009 Volltext http://publications.lib.chalmers.se/publication/246821-isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation-from-multispectral GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 35 2017 250-269 |
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ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI |
| abstract |
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). |
| abstractGer |
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). |
| abstract_unstemmed |
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org). |
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Menze, Bjoern H von der Gablentz, Janina Häni, Levin Heinrich, Mattias P Liebrand, Matthias Winzeck, Stefan Basit, Abdul Bentley, Paul Chen, Liang Christiaens, Daan Dutil, Francis Egger, Karl Feng, Chaolu Glocker, Ben Götz, Michael Haeck, Tom Halme, Hanna-Leena Havaei, Mohammad Iftekharuddin, Khan M Jodoin, Pierre-Marc Kamnitsas, Konstantinos Kellner, Elias Korvenoja, Antti Larochelle, Hugo Ledig, Christian Lee, Jia-Hong Maes, Frederik Mahmood, Qaiser Maier-Hein, Klaus H McKinley, Richard Muschelli, John Pal, Chris Pei, Linmin Rangarajan, Janaki Raman Reza, Syed M.S Robben, David Rueckert, Daniel Salli, Eero Suetens, Paul Wang, Ching-Wei Wilms, Matthias Kirschke, Jan S Krämer, Ulrike M Münte, Thomas F Schramm, Peter Wiest, Roland Handels, Heinz Reyes, Mauricio |
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Menze, Bjoern H von der Gablentz, Janina Häni, Levin Heinrich, Mattias P Liebrand, Matthias Winzeck, Stefan Basit, Abdul Bentley, Paul Chen, Liang Christiaens, Daan Dutil, Francis Egger, Karl Feng, Chaolu Glocker, Ben Götz, Michael Haeck, Tom Halme, Hanna-Leena Havaei, Mohammad Iftekharuddin, Khan M Jodoin, Pierre-Marc Kamnitsas, Konstantinos Kellner, Elias Korvenoja, Antti Larochelle, Hugo Ledig, Christian Lee, Jia-Hong Maes, Frederik Mahmood, Qaiser Maier-Hein, Klaus H McKinley, Richard Muschelli, John Pal, Chris Pei, Linmin Rangarajan, Janaki Raman Reza, Syed M.S Robben, David Rueckert, Daniel Salli, Eero Suetens, Paul Wang, Ching-Wei Wilms, Matthias Kirschke, Jan S Krämer, Ulrike M Münte, Thomas F Schramm, Peter Wiest, Roland Handels, Heinz Reyes, Mauricio |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1989760058</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715030331.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170207s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.media.2016.07.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20170501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1989760058</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1989760058</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1589-c3dba17c3c93d0c4e6f6b48681205a461258ad30769c14e25d18c4a48b00463d0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0392983320170000035000000250isles2015apublicevaluationbenchmarkforischemicstro</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ZDB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Maier, Oskar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Menze, Bjoern H</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">von der Gablentz, Janina</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Häni, Levin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Heinrich, Mattias P</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liebrand, Matthias</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Winzeck, Stefan</subfield><subfield code="4">oth</subfield></datafield><datafield 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