基于深度学习的医疗图像分割综述

自2006年深度学习这一概念提出以来,各研究领域对于深度学习技术的研究热度一直高居不下.深度学习的出现,对计算机视觉领域的发展起到了重要推动作用.计算机视觉的主要研究任务是对图像、视频等进行目标的检测、识别以及分割等,目前已经广泛应用于医疗、金融和工业领域中.其中最常见的应用场景是医学图像处理.图像分割是医学图像处理任...

A Survey on Medical Image Segmentation Based on Deep Learning

Since the concept of deep learning was proposed in 2006,research enthusiasm for deep learning technology in various fields has been high.The emergence of deep learning has played an important role in promoting the development of computer vision.The main task of computer vision is to detect,identify or segment objects in images and videos.It has been widely used in medical,financial and industrial fields,and the most common application scenario is medical image processing.Image segmentation is an important research direction in medical image processing tasks.Current image segmentation methods include methods of traditional and methods based on deep learning.Firstly,we introduce traditional image segmentation methods such as threshold segmentation,region growing and graph cutting.Secondly,we summarize the network architecture of FCN,U-Net,U-Net++,SegNet and DeepLab,and analyze their advantages and disadvantages.Next,we focus on the application of image segmentation method in medical image processing.Finally,we discuss future challenges and development opportunities for medical image analysis based on deep learning.