Research on U-Net medical image segmentation

Abstract

With the rapid development of deep learning technology and medical technology, neural networks are widely used in the field of medical image segmentation. Among them, U-Net neural network has gradually become a research hotspot in the field of image segmentation because of its good segmentation performance. It provides doctors with a consistent method of quantifying lesions and is widely used in the field of medical image semantic segmentation. This article studies the U-Net network, learns theoretically from the U-Net network model and its basic principles, and then conducts experiments on three typical medical images of liver medical images, fundus blood vessel images, and lung nodule images to explain various types of medical images. The characteristics of the image and the difficulty of segmentation, and the performance of the U-Net network in the relevant image segmentation is verified. Finally, the problems existing in U-Net network are discussed, and the future development is prospected.