Synthetic Data for Robust Stroke Segmentation
Liam Chalcroft Ioannis Pappas Cathy J. Price John Ashburner
Deep learning-based semantic segmentation in neuroimaging currently requires
high-resolution scans and extensive annotated datasets, posing significant
barriers to clinical applicability. We present a novel synthetic framework for
the task of lesion segmentation, extending the capabilities of the established
SynthSeg approach to accommodate large heterogeneous pathologies with
lesion-specific augmentation strategies. Our method trains deep learning
models, demonstrated here with the UNet architecture, using label maps derived
from healthy and stroke datasets, facilitating the segmentation of both healthy
tissue and pathological lesions without sequence-specific training data.
Evaluated against in-domain and out-of-domain (OOD) datasets, our framework
demonstrates robust performance, rivaling current methods within the training
domain and significantly outperforming them on OOD data. This contribution
holds promise for advancing medical imaging analysis in clinical settings,
especially for stroke pathology, by enabling reliable segmentation across
varied imaging sequences with reduced dependency on large annotated corpora.
Code and weights available at https://github.com/liamchalcroft/SynthStroke.