Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data

• URL: http://arxiv.org/abs/2412.03318v3
• Date: Sun, 01 Jun 2025 00:17:19 GMT
• Title: Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
• Authors: Liam Chalcroft, Jenny Crinion, Cathy J. Price, John Ashburner,
• Abstract summary: We introduce two physics-constrained approaches to generate synthetic quantitative MRI (qMRI) images.<n>Our first method, $textttqATLAS$, trains a neural network to estimate qMRI maps from standard MPRAGE images.<n>The second method, $textttq Synth$, synthesises qMRI maps directly from tissue labels.
• Score: 0.15749416770494706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approaches to generate synthetic quantitative MRI (qMRI) images that improve segmentation robustness across heterogeneous domains. Our first method, $\texttt{qATLAS}$, trains a neural network to estimate qMRI maps from standard MPRAGE images, enabling the simulation of varied MRI sequences with realistic tissue contrasts. The second method, $\texttt{qSynth}$, synthesises qMRI maps directly from tissue labels using label-conditioned Gaussian mixture models, ensuring physical plausibility. Extensive experiments on multiple out-of-domain datasets show that both methods outperform a baseline UNet, with $\texttt{qSynth}$ notably surpassing previous synthetic data approaches. These results highlight the promise of integrating MRI physics into synthetic data generation for robust, generalisable stroke lesion segmentation. Code is available at https://github.com/liamchalcroft/qsynth

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