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de la Rosa, E.* ; Reyes, M.* ; Liew, S.L.* ; Hutton, A.* ; Wiest, R.* ; Kaesmacher, J.* ; Hanning, U.* ; Hakim, A.J.* ; Zubal, R.* ; Valenzuela, W.* ; Robben, D.* ; Sima, D.M.* ; Anania, V.* ; Brys, A.* ; Meakin, J.A.* ; Mickan, A.* ; Broocks, G.* ; Heitkamp, C.* ; Gao, S.* ; Liang, K.W.* ; Zhang, Z.* ; Rahman Siddiquee, M.M.* ; Myronenko, A.* ; Ashtari, P.* ; Van Huffel, S.* ; Jeong, H.* ; Yoon, C.* ; Kim, C.* ; Huo, J.* ; Ourselin, S.* ; Sparks, R.* ; Clèrigues, A.* ; Oliver, A.J.* ; Lladó, X.* ; Chalcroft, L.* ; Pappas, I.* ; Bertels, J.* ; Heylen, E.* ; Moreau, J.* ; Hatami, N.* ; Frindel, C.* ; Qayyum, A.* ; Mazher, M.* ; Puig, D.* ; Lin, S.C.* ; Juan, C.J.* ; Hu, T.* ; Boone, L.* ; Goubran, M.* ; Liu, Y.J.* ; Wegener, S.* ; Kofler, F. ; Ezhov, I.* ; Shit, S.* ; Hernandez Petzsche, M.R.* ; Müller, M.* ; Menze, B.* ; Kirschke, J.S.* ; Wiestler, B.*

DeepISLES: A clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.

Nat. Commun. 16:7357 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Diffusion-weighted MRI is critical for diagnosing and managing ischemic stroke, but variability in images and disease presentation limits the generalizability of AI algorithms. We present DeepISLES, a robust ensemble algorithm developed from top submissions to the 2022 Ischemic Stroke Lesion Segmentation challenge we organized. By combining the strengths of best-performing methods from leading research groups, DeepISLES achieves superior accuracy in detecting and segmenting ischemic lesions, generalizing well across diverse axes. Validation on a large external dataset (N = 1685) confirms its robustness, outperforming previous state-of-the-art models by 7.4% in Dice score and 12.6% in F1 score. It also excels at extracting clinical biomarkers and correlates strongly with clinical stroke scores, closely matching expert performance. Neuroradiologists prefer DeepISLES' segmentations over manual annotations in a Turing-like test. Our work demonstrates DeepISLES' clinical relevance and highlights the value of biomedical challenges in developing real-world, generalizable AI tools. DeepISLES is freely available at https://github.com/ezequieldlrosa/DeepIsles .
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Publication type Article: Journal article
Document type Scientific Article
Keywords Lesion Segmentation; Computed-tomography; Benchmark; Association; Images; Core
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 16, Issue: 1, Pages: , Article Number: 7357 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530001-001
Grants Korea Evaluation Institute of Industrial Technology (KEIT) - Korea government (MOTIE)
Artificial Intelligence Graduate School Program (POSTECH)
Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government (MSIT)
Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education
Swiss Heart Foundation
Swiss National Science Foundation
National Institutes of Health, National Institutes of Neurological Disorders and Stroke (NIH NINDS)
Flemish Government (AI Research Program)
Helmut Horten Foundation
DOI 10.1038/s41467-025-62373-x
WOS ID 001547173700026
Scopus ID 105012936890
PubMed ID 40783484
Erfassungsdatum 2025-10-10
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