计算机应用 ›› 2019, Vol. 39 ›› Issue (7): 2121-2124.DOI: 10.11772/j.issn.1001-9081.2018112321

• 虚拟现实与多媒体计算 • 上一篇    下一篇

基于卷积神经网络的超声图像左心室分割方法

朱锴1,2, 付忠良1,2, 陈晓清1,2   

  1. 1. 中国科学院 成都计算机应用研究所, 成都 610041;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2018-11-23 修回日期:2018-12-28 出版日期:2019-07-10 发布日期:2019-07-15
  • 通讯作者: 朱锴
  • 作者简介:朱锴(1991-),男,贵州安顺人,博士研究生,主要研究方向:机器视觉、机器学习;付忠良(1967-),男,重庆合川人,研究员,博士生导师,主要研究方向:机器学习;陈晓清(1989-),男,四川凉山人,博士研究生,主要研究方向:机器学习、机器视觉。
  • 基金资助:

    四川省科技厅重点研发项目(2017SZ0010);四川省科技支撑计划项目(2016JZ0035)。

Left ventricular segmentation method of ultrasound image based on convolutional neural network

ZHU Kai1,2, FU Zhongliang1,2, CHEN Xiaoqing1,2   

  1. 1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-11-23 Revised:2018-12-28 Online:2019-07-10 Published:2019-07-15
  • Supported by:

    This work is partially supported by the Key Research and Development Project of Sichuan Science and Technology Department (2017SZ0010), the Science and Technology Support Program of Sichuan Province (2016JZ0035).

摘要:

超声图像左心室的分割在临床上对医生的作用巨大。由于超声图像含有大量噪声,轮廓特征不明显,目前的卷积神经网络(CNN)方法对左心室分割容易得到不必要的区域,并且分割目标不完整。为了解决上述问题,在全卷积神经网络(FCN)基础上加入了关键点定位和求取图像凸包方法对分割结果进行优化。首先采用FCN获取初步的分割结果;然后为了去除分割结果中的错误区域,提出一种CNN定位左心室三个关键点的位置,通过关键点筛选掉分割结果中不必要的区域;最后为保证剩余区域能够组合成一个完整的心室,利用求取图像凸包算法将所有有效区域进行合并。实验结果表明,在超声图像左心室分割效果上,所提方法能够在普通FCN的基础上获得很大的提升,在交并比评价标准下,该方法获取的左心室结果能够比传统CNN方法提升近15%。

关键词: 超声图像, 分割, 关键点定位, 卷积神经网络, 凸包

Abstract:

Ultrasound image segmentation of left ventricle is very important for doctors in clinical practice. As the ultrasound images contain a lot of noise and the contour features are not obvious, current Convolutional Neural Network (CNN) method is easy to obtain unnecessary regions in left ventricular segmentation, and the segmentation regions are incomplete. In order to solve these problems, keypoint location and image convex hull method were used to optimize segmentation results based on Fully Convolutional neural Network (FCN). Firstly, FCN was used to obtain preliminary segmentation results. Then, in order to remove erroneous regions in segmentation results, a CNN was proposed to locate three keypoints of left ventricle, by which erroneous regions were filtered out. Finally, in order to ensure that the remained area were able to be a complete ventricle, image convex hull algorithm was used to merge all the effective areas together. The experimental results show that the proposed method can greatly improve left ventricular segmentation results of ultrasound images based on FCN. Under the evaluation standard, the accuracy of results obtained by this method can be increased by nearly 15% compared with traditional CNN method.

Key words: ultrasound image, segmentation, keypoint location, Convolutional Neural Network (CNN), convex hull

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