Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation  

作　　者：Hongxia Deng Yuefang Zhang Ran Li Chunxiang Hu Zijian Feng Haifang Li 

机构地区：[1]the School of Information and Computer,Taiyuan University of Technology,Taiyuan 030600,China

出　　处：《Tsinghua Science and Technology》2022年第1期68-78,共11页清华大学学报（自然科学版（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(Nos.61873178and 61976150);Natural Science Foundation of Shanxi Province(Nos.201801D21135 and 201901D111091);Key Research and Development Projects of Shanxi Province(No.201803D421047)。

摘　　要：This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus.Different convolutional configurations were proposed to capture information obtained by a segmentation network.In addition,a generative adversarial network based on Pixel2Pixel was proposed.The generator was a codec structure combining a residual network and an attention mechanism to capture detailed information.The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the expert.Through the continuously transmitted losses of the generator and discriminator,the generator reached the optimal state of hippocampus segmentation.T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer’s disease Neuroimaging Initiative dataset were used as training and test data;similarity coefficient,sensitivity,and positive predictive value were used as evaluation indicators.Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases,such as Alzheimer’s disease.

关 键 词：magnetic resonance imaging generative adversarial network residual network attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] R749.16[医药卫生—神经病学与精神病学]