3D Graph-Connectivity Constrained Network for Hepatic Vessel Segmentation

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 3; pp. 1251 - 1262
• Main Authors: Li, Ruikun, Huang, Yi-Jie, Chen, Huai, Liu, Xiaoqing, Yu, Yizhou, Qian, Dahong, Wang, Lisheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biomedical imaging; Blood vessels; Computed tomography; Connectivity; Convolutional neural networks; graph neural network; Graph theory; graphical connectivity; Hepatic vessel segmentation; Humans; Image contrast; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging, Three-Dimensional; Inference; Liver cancer; Medical imaging; Neural networks; Neural Networks, Computer; prior constraints; Three-dimensional displays; Training
• DOI: 10.1109/JBHI.2021.3118104
• ISSN: 2168-2194

Segmentation of hepatic vessels from 3D CT images is necessary for accurate diagnosis and preoperative planning for liver cancer. However, due to the low contrast and high noises of CT images, automatic hepatic vessel segmentation is a challenging task. Hepatic vessels are connected branches containing thick and thin blood vessels, showing an important structural characteristic or a prior: the connectivity of blood vessels. However, this is rarely applied in existing methods. In this paper, we segment hepatic vessels from 3D CT images by utilizing the connectivity prior. To this end, a graph neural network (GNN) used to describe the connectivity prior of hepatic vessels is integrated into a general convolutional neural network (CNN). Specifically, a graph attention network (GAT) is first used to model the graphical connectivity information of hepatic vessels, which can be trained with the vascular connectivity graph constructed directly from the ground truths. Second, the GAT is integrated with a lightweight 3D U-Net by an efficient mechanism called the plug-in mode, in which the GAT is incorporated into the U-Net as a multi-task branch and is only used to supervise the training procedure of the U-Net with the connectivity prior. The GAT will not be used in the inference stage, and thus will not increase the hardware and time costs of the inference stage compared with the U-Net. Therefore, hepatic vessel segmentation can be well improved in an efficient mode. Extensive experiments on two public datasets show that the proposed method is superior to related works in accuracy and connectivity of hepatic vessel segmentation.