U型卷积网络在乳腺医学图像分割中的研究综述OA北大核心CSTPCD

U-Net and its variants have showcased exceptional performance in the domain of breast medical image segmentation.By employing a fully convolutional network(FCN)structure for semantic segmentation,the symmet-rical structure of U-Net offers remarkable flexibility and adaptability.It can be tailored to diverse image segmenta-tion tasks and challenges by adjusting network depth and incorporating new modules,leaving a significant impact on subsequent network designs.This paper aims to delve into the application of U-shaped convolutional networks in breast medical image segmentation,categorizing and summarizing U-shaped convolutional networks used for this purpose in recent years.It outlines the widely used breast medical image datasets and evaluation metrics,discusses common data augmentation techniques,and provides a detailed introduction to the network structure of the U-Net model along with traditional segmentation methods for breast medical images.Furthermore,it summarizes the im-provements made to the U-Net network structure for breast medical image segmentation,including modifications like residual structures,multi-scale features,dilation mechanisms,attention mechanisms,skip connection mecha-nisms,and integration with Transformers.Finally,it addresses the current challenges and problems encountered in breast medical image segmentation and offers insights into future research directions.