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

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.

Key words: medical image segmentation, magnetic resonance imaging (MRI), convolutional neural network, long short-term memory (LSTM), multi-view ensemble