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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

吴玉超, 林岚, 王婧璇, 吴水才. 基于卷积神经网络的语义分割在医学图像中的应用[J]. 仁和测试, 2020, 37(3): 533-540. doi: 10.7507/1001-5515.201906067
引用本文: 吴玉超, 林岚, 王婧璇, 吴水才. 基于卷积神经网络的语义分割在医学图像中的应用[J]. 仁和测试, 2020, 37(3): 533-540. doi: 10.7507/1001-5515.201906067
Yuchao WU, Lan LIN, Jingxuan WANG, Shuicai WU. Application of semantic segmentation based on convolutional neural network in medical images[J]. Rhhz Test, 2020, 37(3): 533-540. doi: 10.7507/1001-5515.201906067
Citation: Yuchao WU, Lan LIN, Jingxuan WANG, Shuicai WU. Application of semantic segmentation based on convolutional neural network in medical images[J]. Rhhz Test, 2020, 37(3): 533-540. 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
基金项目: 国家科技支撑计划课题(2015BAI02B03);国家自然科学基金(81971683);北京市自然科学基金-海淀原始创新联合基金(L182010);北京市教委科技一般项目(KM201810005033)
详细信息
    通讯作者:

    林岚,Email:lanlin@bjut.edu.cn

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

Funds: National Science and Technology Support Program; Beijing Natural Science Foundation; Grants from the Scientific Research General Project of Beijing Municipal Education Committee
More Information
  • 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。
  • 图  1  CNN 结构图

    Figure  1.  A schematic of CNN architecture

    图  2  FCN 卷积化、跳跃结构示意图

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

    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。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-24
  • 修回日期:  2020-01-29
  • 刊出日期:  2020-03-17

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