Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool

• URL: http://arxiv.org/abs/2510.24378v1
• Date: Tue, 28 Oct 2025 12:56:48 GMT
• Title: Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool
• Authors: Yann Kerverdo, Florent Leray, Youwan Mahé, Stéphanie Leplaideur, Francesca Galassi,
• Abstract summary: Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically.<n>We introduce textitStrokeSeg, a modular and lightweight framework that translates research-grade stroke lesion segmentation models into deployable applications.
• Score: 0.08699280339422537
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $<10^{-3}$), demonstrating that high-performing research pipelines can be transformed into portable, clinically usable tools.

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