基于卷积神经网络的语义分割在医学图像中的应用

吴玉超; 林岚; 王婧璇; 吴水才

doi:10.7507/1001-5515.201906067

基于卷积神经网络的语义分割在医学图像中的应用

doi: 10.7507/1001-5515.201906067

详细信息

通讯作者:
林岚，Email：lanlin@bjut.edu.cn

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出版历程
- 收稿日期: 2019-06-24
- 修回日期: 2020-01-29
- 发布日期: 2020-03-17

Application of semantic segmentation based on convolutional neural network in medical images

More Information

Corresponding author: LIN lan, Email：lanlin@bjut.edu.cn

摘要

摘要: 随着网络结构的迅速发展，卷积神经网络（CNN）在图像分析领域已成为一种领先的机器学习工具。因此，基于 CNN 的语义分割也已成为医学图像理解中的一项关键高级任务。本文综述了基于 CNN 的语义分割在医学图像领域中的研究进展，回顾了多种经典的语义分割方法及其架构变化，并重点介绍了它们在该领域的贡献和意义。在此基础上，进一步总结和讨论了它们在一些重要的生理与病理解剖结构分割中的应用。最后，本文讨论了语义分割在医学图像领域应用将遭遇的挑战和潜在发展方向。
- 卷积神经网络 /
- 语义分割 /
- 医学应用
Abstract: With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.
- convolutional neural network /
- semantic segmentation /
- medical application

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图 1 CNN 结构图

Figure 1. A schematic of CNN architecture

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图 2 FCN 卷积化、跳跃结构示意图

Figure 2. The schematic of convolutional and skip architecture of FCN

下载: 全尺寸图片幻灯片

表 1 语义分割网络优化及架构比较

Table 1. Semantic segmentation network optimization and architecture comparison

网络名称	基础 CNN 网络	优化方向	关键特点
U 型网络（U-Net）^[7]（2015）	定制的 CNN	解码网络	将编码器的特征图拼接至每个阶段解码器的上采样特征图，从而形成一个 U 形结构。
金字塔场景解析网络（pyramid scene parsing network，PSPNet）^[8]（2016）	ResNet50	解码网络	金字塔池化模块进行特征融合。
分割网络（SegNet）^[9]（2017）	VGG-16	解码网络	基于编码过程中获得的池化索引来执行非线性上采样。
深度研究实验室（DeepLab 系列网络）^[10-11]（2017）（2018）	VGG-16+ResNet-101	编码网络、独立后处理模块	使用膨胀卷积优化编码网络、独立的条件随机场后处理模块。

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