基于特征融合的海马体分割  

Hippocampus Segmentation Based on Feature Fusion

作　　者：陈嘉敏 张伯泉[1] 麦海鹏 CHEN Jia-min;ZHANG Bo-quan;MAI Hai-peng(School of Computer Science,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广东广州510006

出　　处：《计算机与现代化》2023年第8期1-6,53,共7页Computer and Modernization

基　　金：国家自然科学基金资助项目(62076074);华为“智能基座”人工智能项目(211210176)。

摘　　要：针对现有海马体分割算法无法对目标区域进行精确分割的问题,利用编解码结构研究一种基于特征融合的海马体分割模型。首先利用Resnet34作为模型特征编码层提取更丰富的语义特征;其次在编解码过渡层引入基于混合扩张卷积的ASPP模块以获取多尺度特征信息。最后使用注意力特征融合机制作为编解码层间的连接层以有效结合深层特征与浅层特征,为后续分割提供海马体区域位置信息,提高模型分割性能。实验在ADNI数据集上进行以验证提出模型的有效性,所研究网络模型IoU、DICE、精确率、召回率4个评价指标上分别达到了84.67%、88.51%、87.90%和89.01%。与现有先进医学分割算法进行实验对比,也表明了该模型有更好的分割能力,达到了较好的海马体图像自动分割效果。Aiming at the problem that the existing hippocampal segmentation algorithm can not segment the target accurately,a novel hippocampal segmentation model based on feature fusion using codec structure is studied.Firstly,Resnet34 is used as the model feature encoding layer to extract richer semantic features;Secondly,the ASPP module based on mixed expansion convolu‐tion is introduced into the coding and decoding transition layer to obtain multi-scale feature information.Finally,the attention feature fusion mechanism is used as the connection layer between the encoding and decoding layers to effectively combine the deep features with the shallow features,provide the location information of the hippocampus for subsequent segmentation,and improve the segmentation performance of the model.The experiment is carried out on ADNI dataset to verify the validity of the proposed model.The accuracy of the network model in the four evaluation indicators of IoU,DICE,accuracy and recall rate reaches 84.67%,88.51%,87.90%and 89.01%respectively.Compared with the existing advanced medical segmentation algo‐rithm,the experimental results also show that the model has better segmentation ability and achieves better automatic segmentation effect of hippocampus image.

关 键 词：阿尔兹海默症 海马体分割 注意力机制 特征融合 空洞空间金字塔池化 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]