Synthetic Data for Robust Stroke Segmentation

Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data, limiting clinical applicability. This paper introduces a novel synthetic data framework tailored for stroke lesion segmentation, expanding the SynthSeg methodology to incorporate lesion-specific augmentations that simulate diverse pathological features. Using a modified nnUNet architecture, our approach trains models with label maps from healthy and stroke datasets, facilitating segmentation across both normal and pathological tissue without reliance on specific sequence-based training. Our method achieves robust out-of-domain performance where conventional approaches fail, with in-domain performance of 48.2% Dice compared to 57.5% for conventional training. Crucially, even with oracle knowledge of the optimal domain adaptation method - an unrealistic scenario in practice - conventionally-trained models cannot match our synthetic approach in out-of-domain settings. The framework demonstrates that synthetic pre-training provides fundamental robustness unachievable through test-time adaptation alone. Our approach reduces reliance on domain-specific training data and helps bridge the gap between research-grade and clinical scans to improve clinical stroke neuroimaging workflows. PyTorch training code and weights are publicly available at <a href='https://github.com/liamchalcroft/SynthStroke'>https://github.com/liamchalcroft/SynthStroke</a>, along with an SPM toolbox featuring a plug-and-play model at <a href='https://github.com/liamchalcroft/SynthStrokeSPM'>https://github.com/liamchalcroft/SynthStrokeSPM</a>