Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.
@article{arxiv.1607.00582,
title = {3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes},
author = {Qi Dou and Hao Chen and Yueming Jin and Lequan Yu and Jing Qin and Pheng-Ann Heng},
journal= {arXiv preprint arXiv:1607.00582},
year = {2016}
}