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<p>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 <i>DeepISLES</i>, 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, <i>DeepISLES</i> achieves superior accuracy in detecting and segmenting ischemic lesions, generalizing well across diverse axes. Validation on a large external dataset (<i>N</i> = 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 <i>DeepISLES</i>’ segmentations over manual annotations in a Turing-like test. Our work demonstrates <i>DeepISLES’</i> clinical relevance and highlights the value of biomedical challenges in developing real-world, generalizable AI tools. <i>DeepISLES</i> is freely available at <a href="https://github.com/ezequieldlrosa/DeepIsles">https://github.com/ezequieldlrosa/DeepIsles</a>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月9日

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  • Ezequiel de la Rosa,
  • Mauricio Reyes,
  • Sook-Lei Liew,
  • Alexandre Hutton,
  • Roland Wiest,
  • Johannes Kaesmacher,
  • Uta Hanning,
  • Arsany Hakim,
  • Richard Zubal,
  • Waldo Valenzuela,
  • David Robben,
  • Diana M. Sima,
  • Vincenzo Anania,
  • Arne Brys,
  • James A. Meakin,
  • Anne Mickan,
  • Gabriel Broocks,
  • Christian Heitkamp,
  • Shengbo Gao,
  • Kongming Liang,
  • Ziji Zhang,
  • Md Mahfuzur Rahman Siddiquee,
  • Andriy Myronenko,
  • Pooya Ashtari,
  • Sabine Van Huffel,
  • Hyunsu Jeong,
  • Chiho Yoon,
  • Chulhong Kim,
  • Jiayu Huo,
  • Sebastien Ourselin,
  • Rachel Sparks,
  • Albert Clèrigues,
  • Arnau Oliver,
  • Xavier Lladó,
  • Liam Chalcroft,
  • Ioannis Pappas,
  • Jeroen Bertels,
  • Ewout Heylen,
  • Juliette Moreau,
  • Nima Hatami,
  • Carole Frindel,
  • Abdul Qayyum,
  • Moona Mazher,
  • Domenec Puig,
  • Shao-Chieh Lin,
  • Chun-Jung Juan,
  • Tianxi Hu,
  • Lyndon Boone,
  • Maged Goubran,
  • Yi-Jui Liu,
  • Susanne Wegener,
  • Florian Kofler,
  • Ivan Ezhov,
  • Suprosanna Shit,
  • Moritz R. Hernandez Petzsche,
  • Michael Müller,
  • Bjoern Menze,
  • Jan S. Kirschke,
  • Benedikt Wiestler

摘要

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.