ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.
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DOI：
10.1016/j.media.2016.07.009
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发表时间：
2017-01
影响因子：
10.9
通讯作者：
Reyes, Mauricio
中科院分区：
文献类型：
--
作者：
Maier, Oskar;Menze, Bjoern H.;von der Gablentz, Janina;Hani, Levin;Heinrich, Mattias P.;Liebrand, Matthias;Winzeck, Stefan;Basit, Abdul;Bentley, Paul;Chen, Liang;Christiaens, Daan;Dutil, Francis;Egger, Karl;Feng, Chaolu;Glocker, Ben;Goetz, 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.;Kraemer, Ulrike M.;Muente, Thomas F.;Schramme, Peter;Wiest, Roland;Handels, Heinz;Reyes, Mauricio
缺血性脑卒中是最常见的脑血管疾病，其诊断、治疗和研究依赖于无创成像。从磁共振成像（MRI）体积的中风病变分割算法进行了深入研究，但由于不同的数据集和评估方案，报告的结果在很大程度上是不可比的。我们通过与MICCAI 2015会议联合组织的缺血性卒中病变分割（ISLES）挑战来解决这个紧迫的可比性问题。在本文中，我们提出了一个通用的评估框架，描述了公开可用的数据集，并提出了两个子挑战的结果：亚急性卒中病变分割（SISS）和卒中灌注估计（SPES）。共有16个研究小组参与了广泛的最先进的自动分割算法。对所获得的数据进行全面分析，可以对当前的最新技术水平进行批判性评估，为进一步的发展提出建议，并确定剩余的挑战。在SPES中处理的急性灌注病变的分割被认为是可行的。然而，SISS中应用于亚急性病变分割的算法仍然缺乏准确性。总体而言，没有发现任何方法的算法特征上级其他方法。相反，应该详细研究中风病变外观的特征、其演变以及观察到的挑战。附加说明的ISLES图像数据集继续通过在线评价系统公开提供，作为持续的基准资源（www.isles-challenge.org）。
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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