Synthetic Data for Robust Stroke Segmentation

• URL: http://arxiv.org/abs/2404.01946v2
• Date: Fri, 08 Nov 2024 20:26:20 GMT
• Title: Synthetic Data for Robust Stroke Segmentation
• Authors: Liam Chalcroft, Ioannis Pappas, Cathy J. Price, John Ashburner,
• Abstract summary: Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data. This paper introduces a novel synthetic data framework tailored for stroke lesion segmentation. Our approach trains models with label maps from healthy and stroke datasets, facilitating segmentation across both normal and pathological tissue.
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• Abstract: 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. Evaluation across in-domain and out-of-domain (OOD) datasets reveals that our method matches state-of-the-art performance within the training domain and significantly outperforms existing methods on OOD data. By minimizing dependence on large annotated datasets and allowing for cross-sequence applicability, our framework holds potential to improve clinical neuroimaging workflows, particularly in stroke pathology. PyTorch training code and weights are publicly available at https://github.com/liamchalcroft/SynthStroke, along with an SPM toolbox featuring a plug-and-play model at https://github.com/liamchalcroft/SynthStrokeSPM.

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