DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.
de la Rosa E, Reyes M, Liew SL, Hutton A, Wiest R, Kaesmacher J, Hanning U, Hakim A, Zubal R, Valenzuela W, Robben D, Sima DM, Anania V, Brys A, Meakin JA, Mickan A, Broocks G, Heitkamp C, Gao S, Liang K, Zhang Z, Rahman Siddiquee MM, Myronenko A, Ashtari P, Van Huffel S, Jeong H, Yoon C, Kim C, Huo J, Ourselin S, Sparks R, Clèrigues A, Oliver A, Lladó X, Chalcroft L, Pappas I, Bertels J, Heylen E, Moreau J, Hatami N, Frindel C, Qayyum A, Mazher M, Puig D, Lin SC, Juan CJ, Hu T, Boone L, Goubran M, Liu YJ, Wegener S, Kofler F, Ezhov I, Shit S, Hernandez Petzsche MR, Müller M, Menze B, Kirschke JS, Wiestler B.
de la Rosa E, et al.
Nat Commun. 2025 Aug 9;16(1):7357. doi: 10.1038/s41467-025-62373-x.
Nat Commun. 2025.
PMID: 40783484
Free PMC article.
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 robu …
By combining the strengths of best-performing methods from leading research groups, DeepISLES achieves superior accuracy in detecting and se …