Abstract: 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 the information obtained by the segmentation network. The generative adversarial network based on Pixel2Pixel was proposed. The generator was a codec structure combining residual network and attention mechanism to capture more detailed information. The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and the expert segmentation results. Through generator and discriminator continuously transmitted losses, the generator reached the optimal state of segmenting the hippocampus. Using the T1-weighted MRI scans and related hippocampus labels of 130 healthy subjects from the ADNI data set as training and test data, and the similarity coefficient as the evaluation index, the accuracy rate reached 89.46%. Results showed that the network model could achieve efficient automatic segmentation of hippocampus, which had important practical significance for the correct diagnosis of diseases such as Alzheimer's disease.

Key words: magnetic resonance imaging, generative adversarial network, residual network, attention mechanism, hippocampus