LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation.

Abstract

Myocardium scar segmentation is essential for clinical diagnosis and prognosis for cardiac vascular diseases. Late gadolinium enhancement (LGE) imaging technology has been widely utilized to visualize left atrial and ventricular scars. However, automatic scar segmentation remains challenging due to the imbalance between scar and background and the variation in scar sizes. To address these challenges, we introduce an innovative network, i.e., LGENet, for scar segmentation. LGENet disentangles anatomy and pathology features from LGE images. Note that inherent spatial relationships exist between the myocardium and scarring regions. We proposed a boundary attention module to allow the scar segmentation conditioned on anatomical boundary features, which could mitigate the imbalance problem. Meanwhile, LGENet can predict scar regions across multiple scales with a multi-depth decision module, addressing the scar size variation issue. In our experiments, we thoroughly evaluated the performance of LGENet using LAScarQS 2022 and EMIDEC datasets. The results demonstrate that LGENet achieved promising performance for cardiac scar segmentation.