PCAF‐Net: A liver segmentation network based on deep learning

Abstract

Abstract Liver cancer poses a great threat to people's health. Accurate liver segmentation is crucial to the diagnosis of liver cancer. In recent years, great achievements have been made in liver segmentation using a series of improved networks developed based on U‐Net. U‐Net uses a skip connection splices the feature map in the encoder and decoder. However, this method ignores the difference between the two feature maps, which limits the ability of the network to extract liver structures of different sizes. This study proposes a new network structure, the pyramid convolutional attention fusion network (PCAF‐Net), for 2D liver segmentation. The PCAF mechanism was introduced to fuse the feature maps of different receptive field paths after attention mechanism processing to enhance the semantic expression ability of feature maps in skip connection and to improve the segmentation accuracy. The MICCAI 2017 LiTS dataset and CHAOS were used to validate the proposed method. The results of multi‐index evaluation showed that the segmentation performance of the proposed network was superior to those of other networks. PCAF‐Net achieved accurate liver segmentation, providing a reference for artificial intelligenceassisted clinical diagnosis of liver cancer.

Keywords

• Optical, image and video signal processing
• X‐ray techniques: radiography and computed tomography (biomedical imaging/measurement)
• Computer vision and image processing techniques
• X‐rays and particle beams (medical uses)
• Patient diagnostic methods and instrumentation
• Biology and medical computing