ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Maier, O., Menze, B. H., von der Gablentz, J., Häni, L., Heinrich, M. P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., Christiaens, D., Dutil, F., Egger, K., Feng, C., Glocker, B., Götz, M., Haeck, T., Halme, H., Havaei, M., Iftekharuddin, K. M., Jodoin, P., Kamnitsas, K., Kellner, E., Korvenoja, A., Larochelle, H., Ledig, C., Lee, J., Maes, F., Mahmood, Q., Maier-Hein, K. H., McKinley, R., Muschelli, J., Pal, C., Pei, L., Rangarajan, J. R., Reza, S. M. S., Robben, D., Rueckert, D., Salli, E., Suetens, P., Wang, C., Wilms, M., Kirschke, J. S., Krämer, U. M., Münte, T. F., Schramm, P., Wiest, R., Handels, H., & Reyes, M. Medical Image Analysis, 35:250--269, July, 2016.
doi  abstract   bibtex   
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).
@article{maier_isles_2016,
	title = {{ISLES} 2015 - {A} public evaluation benchmark for ischemic stroke lesion segmentation from multispectral {MRI}},
	volume = {35},
	issn = {1361-8423},
	doi = {10.1016/j.media.2016.07.009},
	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).},
	language = {ENG},
	journal = {Medical Image Analysis},
	author = {Maier, Oskar and Menze, Bjoern H. and von der Gablentz, Janina and Häni, Levin and Heinrich, Mattias P. and Liebrand, Matthias and Winzeck, Stefan and Basit, Abdul and Bentley, Paul and Chen, Liang and Christiaens, Daan and Dutil, Francis and Egger, Karl and Feng, Chaolu and Glocker, Ben and Götz, Michael and Haeck, Tom and Halme, Hanna-Leena and Havaei, Mohammad and Iftekharuddin, Khan M. and Jodoin, Pierre-Marc and Kamnitsas, Konstantinos and Kellner, Elias and Korvenoja, Antti and Larochelle, Hugo and Ledig, Christian and Lee, Jia-Hong and Maes, Frederik and Mahmood, Qaiser and Maier-Hein, Klaus H. and McKinley, Richard and Muschelli, John and Pal, Chris and Pei, Linmin and Rangarajan, Janaki Raman and Reza, Syed M. S. and Robben, David and Rueckert, Daniel and Salli, Eero and Suetens, Paul and Wang, Ching-Wei and Wilms, Matthias and Kirschke, Jan S. and Krämer, Ulrike M. and Münte, Thomas F. and Schramm, Peter and Wiest, Roland and Handels, Heinz and Reyes, Mauricio},
	month = jul,
	year = {2016},
	keywords = {Benchmark, Challenge, Comparison, Ischemic stroke, MRI, Segmentation},
	pages = {250--269}
}
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