2025/10/28 by Bannier, Elise, Kerverdo, Yann, bonan, isabelle +10
Computer Science · Medicine · #Acute Ischemic Stroke Management #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis
paper · pdf · doi:10.48550/arxiv.2510.24378
openalex publication_date 2025/10/28 · openalex created_date 2025/10/30 · openalex updated_date 2026/07/28
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 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 Float16 quantisation, reducing model size by about 50%. 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.