计算机科学与探索2019，Vol.13Issue(9)：1593-1603,11.
改进的卷积神经网络在医学图像分割上的应用
Application of Improved Convolutional Neural Network in Medical Image Seg-mentation??
摘要
Abstract
In order to improve the accuracy and robustness of medical image segmentation, a medical image segmentation method based on improved convolutional neural network is proposed. Firstly, convolution neural network is used to segment the 2D slice sequence in coronal, sagittal and axial views, and then the segmentation results under the three views are integrated to obtain the final results. The convolution neural network is composed of encoding part, bidirectional convolution long short-term memory network (BDC-LSTM) and decoding part. In order to obtain multi-scale information and expand the receptive field of convolution layer, the encoding part uses asymmetric convolution layer and dilated convolution of different sizes. In addition, BDC-LSTM is used to fully extract the relevant information between the slice sequences in a single view between the encoding and decoding parts, thus improving the segmentation accuracy. Taking the hippocampus segmentation as an example, on the ADNI standard dataset, the similarity coefficient, sensitivity and positive prediction rate are used as the evaluation criteria, and the accuracy rates are 89.36% , 88.73% and 90.16% , respectively. The experimental results show that the proposed algorithm is more competitive in accuracy.关键词
医学图像分割/磁共振成像(MRI)/卷积神经网络/长短记忆网络(LSTM)/多视图集成Key words
medical image segmentation/ magnetic resonance imaging (MRI)/ convolutional neural network/ long short-term memory (LSTM)/ multi-view ensemble分类
信息技术与安全科学引用本文复制引用
刘辰,肖志勇,杜年茂..改进的卷积神经网络在医学图像分割上的应用[J].计算机科学与探索,2019,13(9):1593-1603,11.基金项目
The National Natural Science Foundation of China under Grant No. 61601201 (国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Jiangsu Province under Grant No. BK20150160 (江苏省自然科学基金). （江苏省自然科学基金）
