CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Deep learning-based myocardial scar segmentation from late gadolinium
enhancement (LGE) cardiac MRI has shown great potential for accurate and timely
diagnosis and treatment planning for structural cardiac diseases. However, the
limited availability and variability of LGE images with high-quality scar
labels restrict the development of robust segmentation models. To address this,
we introduce CLAIM: \textbf{C}linically-Guided \textbf{L}GE
\textbf{A}ugmentation for Real\textbf{i}stic and Diverse \textbf{M}yocardial
Scar Synthesis and Segmentation framework, a framework for anatomically
grounded scar generation and segmentation. At its core is the SMILE module
(Scar Mask generation guided by cLinical knowledgE), which conditions a
diffusion-based generator on the clinically adopted AHA 17-segment model to
synthesize images with anatomically consistent and spatially diverse scar
patterns. In addition, CLAIM employs a joint training strategy in which the
scar segmentation network is optimized alongside the generator, aiming to
enhance both the realism of synthesized scars and the accuracy of the scar
segmentation performance. Experimental results show that CLAIM produces
anatomically coherent scar patterns and achieves higher Dice similarity with
real scar distributions compared to baseline models. Our approach enables
controllable and realistic myocardial scar synthesis and has demonstrated
utility for downstream medical imaging task.
