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.
Abstract (translated)
基于深度学习的心肌疤痕分割技术在使用延迟钆增强（LGE）心脏MRI进行准确且及时的结构性心脏病诊断和治疗规划方面展现出巨大潜力。然而，高质量心肌疤痕标签的有限可用性和变异性限制了稳健分割模型的发展。为了解决这一问题，我们引入了一个名为CLAIM的新框架：**C**linically-Guided **L**GE **A**ugmentation for Real**i**stic and Diverse **M**yocardial Scar Synthesis and Segmentation（临床指导的LGE增强用于真实且多样的心肌疤痕合成和分割）。该框架旨在生成解剖结构合理、疤痕模式多样的心肌图像。 CLAIM的核心是SMILE模块（Scar Mask generation guided by cLinical knowledgE，基于临床知识引导的心脏疤痕掩模生成），它利用临床上广泛采用的AHA 17段模型作为条件来合成具有解剖一致性和空间多样性心脏疤痕模式的图像。此外，CLAIM还采用了联合训练策略，在该策略中，心肌疤痕分割网络与生成器同步优化，旨在提高生成疤痕的真实感和心肌疤痕分割性能的准确性。 实验结果显示，与基准方法相比，CLAIM能产生结构上连贯的心脏疤痕模式，并且在与真实疤痕分布比较时具有更高的Dice相似度。这种方法使可控且逼真的心肌疤痕合成成为可能，并已在下游医学成像任务中证明了其有效性。
URL
https://arxiv.org/abs/2506.15549
