A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans

Abstract

Liver tumours are diseases with high morbidity and high deterioration probabilities, and accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for quick tumour diagnosis. While 2D network segmentation methods can perform segmentation with lower device performance requirements, they often discard the rich 3D spatial information contained in CT scans, limiting their segmentation accuracy. Hence, a deep residual attention-based U-shaped network (DRAUNet) with a biplane joint method for liver segmentation is proposed in this paper, where the biplane joint method introduces coronal CT slices to assist the transverse slices with segmentation, incorporating more 3D spatial information into the segmentation results to improve the segmentation performance of the network. Additionally, a novel deep residual block (DR block) and dual-effect attention module (DAM) are introduced in DRAUNet, where the DR block has deeper layers and two shortcut paths. The DAM efficiently combines the correlations of feature channels and the spatial locations of feature maps. The DRAUNet with the biplane joint method is tested on three datasets, Liver Tumour Segmentation (LiTS), 3D Image Reconstruction for Comparison of Algorithms Database (3DIRCADb), and Segmentation of the Liver Competition 2007 (Sliver07), and it achieves 97.3%, 97.4%, and 96.9% Dice similarity coefficients (DSCs) for liver segmentation, respectively, outperforming most state-of-the-art networks; this strongly demonstrates the segmentation performance of DRAUNet and the ability of the biplane joint method to obtain 3D spatial information from 3D images.