基于深度学习的医学图像分割技术研究进展

doi:10.16180/j.cnki.issn1007-7820.2021.02.002

Abstract

Abstract:

Medical image segmentation plays an important role in clinical diagnosis and is the basis of other medical image processing methods. With the improvement of computer hardware performance, image segmentation technology based on deep learning has already become a powerful tool for processing medical images and is widely used in various medical image segmentation tasks. This paper introduces several types of common medical images and their characteristics, analyzes and compares the image segmentation algorithms that have emerged in recent years. Some algorithms have been successfully applied to segmentation tasks such as brain tissue, lungs and blood vessels. In response to the current problems in the development of medical image segmentation technology based on deep learning, corresponding strategies are proposed, and the future development direction is also prospected.

Key words: deep learning, medical image, neural network, convolution operation, segmentation algorithm, image processing

CLC Number:

TP391.41

YAN Chao,SUN Zhanquan,TIAN Engang,ZHAO Yangyang,FAN Xiaoyan. Research Progress of Medical Image Segmentation Based on Deep Learning[J].Electronic Science and Technology, 2021, 34(2): 7-11.

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References 32

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网络结构	功能特点	备注
FCN^[12]	密集性预测, 像素级分割	避免了因图像块重叠而导致的重复卷积计算
DeconvNet^[18]	设计了多个反卷积层和反池化层网络	解决了原始FCN网络中的尺寸问题,使物体的细节信息更为详尽
DeepLab^[19]	把卷积神经网络与传统概率图模型融合,并使用了空洞卷积。	在不进行池化的情况下扩大感受野,防止图像局部信息特征的丢失。
SegNet^[20]	提出了最大值池化索引方法	池化过程中丢失的信息可以通过最大值索引在解码阶段得到
PSPNet^[21]	提出金字塔模型提取图像多尺度信息特征	细节特征和全局特征的融合,极大地丰富了图像的语义信息。
U-net^[14]	采用跳跃拼接的编解码网络结构进行特征融合	影响深远,U-net网络被广泛地应用和改进。
V-net^[15]	采用3D 卷积和基于分割衡量指标DICE 系数作为目标函数	专为3D 医学图像分割而设计的改进版U-net网络
U-net++^[16]	把U-net网络结构各层连在一起	U-net网络结构的增强版本
CE-Net^[17]	将密集空洞卷积和多尺度池化相结合	在多种医学图像分割任务上效果显著

Research Progress of Medical Image Segmentation Based on Deep Learning

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