A Framework for Left Atrium Segmentation on CT Images with Combined Detection Network and Level Set Model

Abstract

In this paper, we proposed a framework for left atrium (LA) segmentation on CT with combined detection network and level set model. The proposed framework consists of two steps. Firstly, we trained a Faster RCNN to generate location boxes for LA. The obtained location box can remove unrelated regions to reduce the interference of background and similarity tissues. Secondly, we utilized a self-adapted threshold on the location box to get the initialization for the level set model, which is nearer the LA and more robust than the random and fixed initialization. Then we proposed a 3D level set model with a new edge indicator based on DRLSE for the final LA segmentation. This edge indicator incorporated both numerical and direction information of the data gradient. Hence, the proposed level set model can guide the contour to the correct boundary when there are many boundaries surrounded the object. The framework was trained and evaluated on MICCAI 2013 LA segmentation challenge. The proposed segmentation method achieved the Dice score of 86.46%. Comparing to the original DRLSE, it achieved a 2.72% improvement on the Dice score.