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基于瓶颈残差注意力机制U-net的肝脏肿瘤分割

董晓莹 陈平

董晓莹, 陈平. 基于瓶颈残差注意力机制U-net的肝脏肿瘤分割[J]. CT理论与应用研究, 2021, 30(6): 661-670. doi: 10.15953/j.1004-4140.2021.30.06.01
引用本文: 董晓莹, 陈平. 基于瓶颈残差注意力机制U-net的肝脏肿瘤分割[J]. CT理论与应用研究, 2021, 30(6): 661-670. doi: 10.15953/j.1004-4140.2021.30.06.01
DONG Xiaoying, CHEN Ping. Segmentation of Liver Tumors Based on Bottleneck Residual Attention Mechanism U-net[J]. CT Theory and Applications, 2021, 30(6): 661-670. doi: 10.15953/j.1004-4140.2021.30.06.01
Citation: DONG Xiaoying, CHEN Ping. Segmentation of Liver Tumors Based on Bottleneck Residual Attention Mechanism U-net[J]. CT Theory and Applications, 2021, 30(6): 661-670. doi: 10.15953/j.1004-4140.2021.30.06.01

基于瓶颈残差注意力机制U-net的肝脏肿瘤分割

doi: 10.15953/j.1004-4140.2021.30.06.01
基金项目: 

国家自然科学基金(面向金属基复合材料微结构表征的X射线多谱CT成像方法研究(61801437);基于深度学习的递变能量多谱CT成像表征方法研究(61871351);基于深度学习的低剂量CT重建与影像识别(61971381))。

详细信息
    作者简介:

    董晓莹,女,中北大学硕士研究生在读,主要从事医学图像处理研究,E-mail:727508988@qq.com;陈平*,男,中北大学教授,主要从事X射线成像、光电检测等方面的研究,E-mail:pc0912@163.com。

  • 中图分类号: O242.41;R814

Segmentation of Liver Tumors Based on Bottleneck Residual Attention Mechanism U-net

  • 摘要: 医学CT图像中含有的大量噪声以及肝脏肿瘤大小不均一、位置因人而异且与相邻器官较相似的组织密度等都造成肝脏肿瘤分割困难。现有传统全卷积神经网络(FCN)方法,通过为输入CT图像中每个像素分配类别标签来实现肝脏肿瘤分割,但在分割精度上仍会出现小目标漏检或目标边界分割模糊的问题。针对这些问题,本文提出一种瓶颈残差注意力机制U-net(BRA U-net)的肝脏肿瘤分割方法,通过引入瓶颈残差模块可在非常深的网络中大幅减少计算量的同时解决梯度消失问题,此外堆叠的注意力模块可以增大有效特征的比重。本文在公共MICCAI2017肝肿瘤分割数据集上测试了该框架,戴斯相似性系数值达到0.788,高于其他对比分割网络,并利用3D-IRCADb数据集来验证该方法的一般性,结果表明本文方法分割效果良好,能够为临床诊断提供可靠依据。

     

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    DOI:10.3778/j.issn.1002-8331.2003-0370. YAN A, WANG W W. Segmentation of liver and liver tumor based on conditional energy-based GAN[J]. Computer Engineering and Applications, 2021, 57(11):179-184. DOI:10.3778/j.issn. 1002-8331.2003-0370.
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出版历程
  • 收稿日期:  2021-07-27
  • 网络出版日期:  2021-11-04

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