井冈山大学学报（自然科学版）2025，Vol.46Issue(6)：63-70,8.DOI:10.3969/j.issn.1674-8085.2025.06.007
一种轻量化多尺度注意力引导的肝脏及肿瘤CT分割网络
A lightweight multi-scale attention-guided network for liver and tumor segmentation in CT images
摘要
Abstract
To address the challenges of boundary ambiguity,multi-scale target variation,and low contrast in liver and tumor CT image segmentation,this paper presents a lightweight multi-scale attention-guided network termed MSAU-Net.Specifically,depthwise separable convolution(DWConv)is utilized to reduce the model parameters and computational complexity while preserving feature representation capability.A multi-scale dilated convolution module(MSDConv)is designed to extract the features across multiple receptive fields in parallel,thus improving the detection of small targets.Additionally,a multi-scale adaptive hierarchical attention module(MSA-HAM)is integrated into the bottleneck to reweight the features across spatial and channel dimensions.Attention-guided skip connections(AG)are further employed to enhance the contrast between the foreground and background regions.The experimental results on the SLIVER07 and LiTS17 datasets demonstrate that MSAU-Net achieves Dice coefficients of 0.977 2 and 0.965 7 for liver segmentation and 0.876 2 for tumor segmentation,surpassing the existing mainstream methods.The proposed model offers a favorable balance between the segmentation accuracy and the computational efficiency,which is suitable for deployment in resource-constrained environments for the intelligent medical image segmentation.关键词
肝脏CT分割/肝肿瘤分割/多尺度空洞卷积/多尺度自适应层级注意力Key words
liver CT segmentation/liver tumor segmentation/multi-scale dilated convolution/multi-scale adaptive hierarchical attention分类
信息技术与安全科学引用本文复制引用
方文胜,杨庆苒,徐争元..一种轻量化多尺度注意力引导的肝脏及肿瘤CT分割网络[J].井冈山大学学报（自然科学版）,2025,46(6):63-70,8.基金项目
安徽省高等学校科学研究项目(自然科学类)重点项目(2024AH051916) （自然科学类）
皖南医学院校中青年科研基金项目(WK202203) （WK202203）
