郑州大学学报（工学版）2025，Vol.46Issue(5)：26-34,9.DOI:10.13705/j.issn.1671-6833.2025.02.013
基于多视图融合和2.5D U-Net的海马体图像分割
Hippocampus Image Segmentation Based on Multi-view Fusion and 2.5D U-Net
摘要
Abstract
Aiming at the problem in existing methods of automatic segmentation of hippocampus image,which can not make good use of the context information,might lead to the difficulty in improving the segmentation accuracy and large memory consumption in the process of training and detection,a new model called MVF-2.5D U-Net based on multi-view fusion and 2.5D U-Net was introduced.Firstly,this model improved the 2D U-Net by incorpo-rating a Triplet Attention module and adjusting the depth of the network.Secondly,the traditional single-slice input was replaced by a three-channel 2.5D image composed of adjacent slices.Finally,a volume fusion network was constructed to replace the conventional majority voting scheme.This study was validated by cross-validation on the HarP dataset.The experimental results showed that the average Dice coefficient and Hausdorff distance of the model on the hippocampus image segmentation task were 0.902 and 3.02,respectively,the accuracy and stability was better than the traditional U-Net model and comparison algorithm,and it was also suitable for the resource-con-strained situation,which proved that the proposed model could achieve hippocampus segmentation on MRI more ef-fectively.关键词
海马体图像分割/卷积神经网络/U-Net/Triplet Attention/注意力机制/体积融合网络Key words
hippocampus image segmentation/CNN/U-Net/Triplet Attention/attention mechanism/volume fu-sion network分类
信息技术与安全科学引用本文复制引用
陈立伟,彭逸飞,余仁萍,孙源呈..基于多视图融合和2.5D U-Net的海马体图像分割[J].郑州大学学报（工学版）,2025,46(5):26-34,9.基金项目
国家自然科学基金资助项目(62303425) （62303425）
河南省科技攻关项目(242102311015) （242102311015）
河南省重点研发专项项目(231111211600) （231111211600）
