A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI

Abstract

Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared with the human-level performance with the average Dice score of ‘0.9507’ for myocardium, ‘0.7656’ for scar, and ‘0.8377’ for MVO on the validation set consisting of 16 DE-MRI volumes selected from the training EMIDEC dataset. Conclusion: It is concluded that our approach's extensive validation and comprehensive comparison against existing state-of-the-art deep learning models on three annotated datasets, including healthy and diseased exams, make this proposal a reliable tool to enhance MI diagnosis.