Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool
This work addresses the problem of clinical deployment for stroke lesion segmentation tools, offering a practical solution for healthcare professionals, though it is incremental as it focuses on deployment rather than new segmentation methods.
The paper tackled the challenge of deploying deep learning models for stroke lesion segmentation in clinical settings by introducing StrokeSeg, a modular and lightweight framework that achieved equivalent performance to the original PyTorch pipeline with a Dice difference of less than 0.001 on 300 subjects.
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.