  3D printing has been widely adopted for clinical decision making and
interventional planning of Congenital heart disease (CHD), while whole heart
and great vessel segmentation is the most significant but time-consuming step
in the model generation for 3D printing. While various automatic 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 the 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 683D CT images covering 14 types of CHD show that our method can increase
Dice score by 11.9% on average compared with the state-of-the-art whole heart
and great vessel segmentation method in normal anatomy. The segmentation
results are also printed out using 3D printers for validation.
