# Accurate Congenital Heart Disease Model Generation for 3D Printing

## TL;DR

This paper presents a novel framework combining deep learning and graph algorithms to improve segmentation of the heart and vessels in CHD, enabling more accurate 3D printed models for clinical use.

## Contribution

It introduces a hybrid approach that effectively handles the structural variations in CHD for better segmentation accuracy compared to existing methods.

## Key findings

- Increased Dice score by 11.9% on average over state-of-the-art methods.
- Successfully applied to 683 CT images covering 14 CHD types.
- Validated segmentation results through 3D printing.

## Abstract

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.