Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching

Abstract

Congenital heart disease (CHD) is the leading cause of mortality from birth defects, which occurs 1 in every 110 births in the United States. While various whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processing regular structures and that of graph algorithms in dealing with large variations, and propose a framework that combines both for whole heart and great vessel segmentation in CHD. Particularly, we first use deep learning to segment the four chambers and myocardium followed by blood pool, where variations are usually small. We then extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results using 68 3D CT images covering 14 types of CHD show that our method can increase Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. Our dataset is released to the public.