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

arXiv: https://arxiv.org/abs/2510.24378 · HTML: https://mlsanity.com/paper/2510.24378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis](https://mlsanity.com/paper/2010.03639/) (2020)

## Built on by (most cited first, 1 of 1)

- [StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows](https://mlsanity.com/paper/2607.19901/) (2026)

## Abstract (arXiv)

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 textitStrokeSeg, 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 textttFloat16 quantisation, reducing model size by about 50%. textitStrokeSeg 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.