Human Brain Hippocampus Segmentation Based on Improved U-net Model

Abstract

The hippocampus segmentation in MRI is of great significance for the diagnosis, treatment decision and research of neuropsychiatric diseases. Manual segmentation of the hippocampus is very time-consuming and has low repeatability. With the development of deep learning, great progress has been brought about in this regard. In this paper, the U-net model is selected to realize the automatic segmentation of the hippocampus, and the residual module is added to the U-net segmentation network to speed up the network convergence. Aiming at the characteristics of the hippocampus in the brain MRI image such as blurry edges, irregular shapes, and small size, the Laplacian algorithm is used to sharpen and filter the original image to make the details and edges of the brain image clearer. The enhanced picture can effectively improve the segmentation effect. Finally, the Dice coefficient on the test set reached 90.14%.The experimental results show that the pre-processed images use this segmentation model to achieve accurate segmentation of the hippocampus in the brain MRI, which can assist doctors in better diagnosis.