PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images

• Published in: Medical physics (Lancaster) Vol. 48; no. 7; pp. 3752 - 3766
• Main Authors: Xu, Yingying, Cai, Ming, Lin, Lanfen, Zhang, Yue, Hu, Hongjie, Peng, Zhiyi, Zhang, Qiaowei, Chen, Qingqing, Mao, Xiongwei, Iwamoto, Yutaro, Han, Xian‐Hua, Chen, Yen‐Wei, Tong, Ruofeng
• Format: Journal Article
• Language: English
• Published: 01.07.2021
• Subjects: 3D boundary‐enhanced loss; liver tumor segmentation; multiphase CT; multiscale fusion; phase attention
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.14922

Purpose Liver tumor segmentation is a crucial prerequisite for computer‐aided diagnosis of liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase CT images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multiphase information is ignored. Therefore, it is highly demanded to develop a method effectively incorporating multiphase information for automatic and accurate liver tumor segmentation. Methods In this paper, we propose a phase attention residual network (PA‐ResSeg) to model multiphase features for accurate liver tumor segmentation. A phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intraphase attention (intra‐PA) module and an interphase attention (inter‐PA) module to capture channel‐wise self‐dependencies and cross‐phase interdependencies, respectively. Thus, it enables the network to learn more representative multiphase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA‐based multiscale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multiscale features from multiphase images. Moreover, a 3D boundary‐enhanced loss (BE‐loss) is proposed for training to make the network more sensitive to boundaries. Results To evaluate the performance of our proposed PA‐ResSeg, we conducted experiments on a multiphase CT dataset of focal liver lesions (MPCT‐FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.7787, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328, and a relative volume difference (RVD) of 0.0443 on the MPCT‐FLLs. Furthermore, to validate the effectiveness and robustness of PA‐ResSeg, we conducted extra experiments on another multiphase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637, and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones. Conclusions The study demonstrates that our method can effectively model information from multiphase CT images to segment liver tumors and outperforms other state‐of‐the‐art methods. The PA‐based MSF method can learn more representative multiphase features at multiple scales and thereby improve the segmentation performance. Besides, the proposed 3D BE‐loss is conducive to tumor boundary segmentation by enforcing the network focus on boundary regions and marginal slices. Experimental results evaluated by quantitative metrics demonstrate the superiority of our PA‐ResSeg over the best‐known methods.