计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 95-100.doi: 10.11896/jsjkx.200700067

• 图像处理&多媒体技术 • 上一篇    下一篇

基于多级特征和全局上下文的纵膈淋巴结分割算法

徐少伟1,2, 秦品乐1,2, 曾建朝1,2, 赵致楷3, 高媛1,2, 王丽芳1,2   

  1. 1 中北大学大数据学院 太原030051
    2 中北大学山西省医学影像人工智能工程技术研究中心 太原030051
    3 山西医科大学第一医院 太原030001
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 曾建朝(zjc@nuc.edu.cn)
  • 作者简介:meerkatx@163.com
  • 基金资助:
    山西省工程技术研究中心建设项目(201805D121008)

Mediastinal Lymph Node Segmentation Algorithm Based on Multi-level Features and Global Context

XU Shao-wei1,2, QIN Pin-le1,2, ZENG Jian-chao1,2, ZHAO Zhi-kai3, GAO Yuan1,2, WANG Li-fang1,2   

  1. 1 School of Data Science,North University of China,Taiyuan 030051,China
    2 Shanxi Medical Imaging and Data Analysis Engineering Research Center,North University of China,Taiyuan 030051,China
    3 First Hospital of Shanxi Medical University,Taiyuan 030001,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:XU Shao-wei,born in 1995,M.S.candidate,is a member of China Computer Federation.His main research interests include computer vision,medical image and machine learning.
    ZENG Jian-chao,born in 1963,Ph.D,is a member of China Computer Federation.His main research interests include medical image and maintenance decision of complex system.
  • Supported by:
    Construction Project of Engineering Technology Research Center of Shanxi Province(201805D121008).

摘要: 针对纵膈淋巴结尺度差异大、正负样本不均衡、软组织和肺肿瘤易混淆的问题,提出一个新颖的用于纵膈淋巴结分割的多级特征和全局上下文分割网络。为了解决纵膈淋巴结正负样本不均衡、与纵膈器官和软组织相似的问题,通过医学先验提取纵膈间隙,减少了纵膈器官干扰。为了解决肿大纵膈淋巴结与肺肿瘤相似、淋巴结出现区域分散的问题,设计了全局上下文模块,通过计算全局上下文依赖,大大提升了网络对淋巴结和背景的分类能力。为了解决纵膈淋巴结尺度差异大的问题,设计了特征融合模块,大大增强了网络对小淋巴结的分割精度。实验表明,所提方法在纵膈淋巴结分割任务中达到了76.92%的准确率,79.65%的召回率和76.08%的dice分数,在准确率、召回率和dice分数上均明显优于当前用于纵膈淋巴结分割的其他算法。

关键词: 3D 卷积神经网络, 计算机辅助诊断, 三维医学影像, 自注意力机制, 纵膈淋巴结分割

Abstract: Aiming at the problems of large mediastinal lymph node scale difference,unbalanced positive and negative samples,and easy to confuse soft tissue and lung tumors,a novel multi-level feature and global context segmentation network for mediastinal lymph node segmentation is proposed.In order to solve the problem that the positive and negative samples of the mediastinal lymph nodes are not balanced and are similar to the mediastinal organs and soft tissues,the mediastinal space is extracted through medical a priori to artificially enhance the attention to the location of the mediastinal lymph nodes.In order to solve the problem that the enlarged mediastinal lymph nodes are similar to lung tumors and the lymph nodes appear regionally dispersed,a global context module is designed.By calculating the global context dependence,the network's ability to classify lymph nodes and background is greatly enhanced.In order to solve the large differences in mediastinal lymph node scale,a feature fusion module was designed to greatly enhance the accuracy of segmentation of small lymph nodes by the network.Experiments show that the proposed method achieves an accuracy rate of 76.92%,a recall rate of 79.65%,and a dice score of 76.08% in the mediastinal lymph node segmentation task.The accuracy rate,recall rate,and dice score are significantly better than other algorithms currently used for mediastinal lymph nodessegmentation.

Key words: 3D convolutional neural network, 3D medical image, Attention mechanism, Computer-aided diagnosis, Mediastinal lymph node segmentation

中图分类号: 

  • TP391.41
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