华南理工大学学报（自然科学版）2025，Vol.53Issue(5)：94-108,15.DOI:10.12141/j.issn.1000-565X.240439
基于多尺度特征融合与重构卷积的肝肿瘤图像分割方法
A Liver Tumor Image Segmentation Method Based on Multi-Scale Feature Fusion and Reconstruction Convolution
摘要
Abstract
Aiming at the problem of insufficient expression ability of liver tumor image features and limited global contextual information transmission,an improved U-Net liver tumor image segmentation method is proposed.Firstly,a low-rank reconstruction convolution is designed to optimize the large number of parameter problems caused by traditional convolution operations,and is used to construct a convolution kernel reconstruction module that uses residual structure to improve the encoder decoder,so that the encoder retains more detailed information and the decoder recovers information more effectively,thereby enhancing the expression ability of liver tumor image features.Then,to enrich the transmission of global contextual information,a three-branch spatial pyramid pooling module is designed to optimize the bottleneck structure of information transmission and to break the limitation of a single path.Secondly,a multi-scale feature fusion module is designed to optimize the reuse mechanism of encoder information,enhance the modeling ability of the model for global contextual information,and improve its efficiency in extracting liver tumor image features in different scales.Finally,the performance of the proposed method is tested on LiTS2017 and 3DIRCADb datasets.Experimental results show that the method achieves a Dice coeffi-cient and an IoU value of 97.56%and 95.25%in the liver image segmentation task on LiTS2017 dataset,and 89.71%and 81.58%in the liver tumor image segmentation task.Moreover,the Dice coefficient and IoU value in the liver image segmentation task on 3DIRCADb dataset respectively reach 97.63%and 95.39%,while respec-tively reach 89.62%and 81.63%in the liver tumor image segmentation task.关键词
肝肿瘤图像分割/卷积核重构/空间金字塔池化/多尺度特征融合Key words
liver tumor image segmentation/convolutional kernel reconstruction/spatial pyramid pooling/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
马金林,酒志青,马自萍,夏明格,张凯,程叶霞,马瑞士..基于多尺度特征融合与重构卷积的肝肿瘤图像分割方法[J].华南理工大学学报（自然科学版）,2025,53(5):94-108,15.基金项目
国家自然科学基金项目(62462001) （62462001）
宁夏回族自治区自然科学基金项目(2024AAC03147) （2024AAC03147）
北方民族大学中央高校基本科研业务费专项资金项目(2023ZRLG02) （2023ZRLG02）
宁夏回族自治区高等学校科学研究项目(NYG2024066) Supported by the National Natural Science Foundation of China(62462001)and the Natural Science Foundation of Ningxia Hui Autonomous Region(2024AAC03147) （NYG2024066）
