{"ID":2849088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24378","arxiv_id":"2510.24378","title":"Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool","abstract":"Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically due to heavy dependencies and monolithic design. We introduce \\textit{StrokeSeg}, a modular and lightweight framework that translates research-grade stroke lesion segmentation models into deployable applications. Preprocessing, inference, and postprocessing are decoupled: preprocessing relies on the Anima toolbox with BIDS-compliant outputs, and inference uses ONNX Runtime with \\texttt{Float16} quantisation, reducing model size by about 50\\%. \\textit{StrokeSeg} provides both graphical and command-line interfaces and is distributed as Python scripts and as a standalone Windows executable. On a held-out set of 300 sub-acute and chronic stroke subjects, segmentation performance was equivalent to the original PyTorch pipeline (Dice difference $\u003c10^{-3}$), demonstrating that high-performing research pipelines can be transformed into portable, clinically usable tools.","short_abstract":"Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically due to heavy dependencies and monolithic design. We introduce \\textit{StrokeSeg}, a modular and lightweight framework that translates research-grade stroke lesion segmenta...","url_abs":"https://arxiv.org/abs/2510.24378","url_pdf":"https://arxiv.org/pdf/2510.24378v1","authors":"[\"Yann Kerverdo\",\"Florent Leray\",\"Youwan Mahé\",\"Stéphanie Leplaideur\",\"Francesca Galassi\"]","published":"2025-10-28T12:56:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}