区域生长全卷积神经网络交互分割肝脏CT图像

张丽娟; 章润; 李东明; 李阳; 王晓坤

doi:10.37188/CJLCD.2020-0338

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区域生长全卷积神经网络交互分割肝脏CT图像

图像处理 | 更新时间：2021-09-10

- 区域生长全卷积神经网络交互分割肝脏CT图像
- Region-growing fully convolutional neural network interactive segmentation of liver CT images
- 液晶与显示 2021年36卷第9期页码：1294-1304
- 作者机构：
  
  1.长春工业大学计算机科学与工程学院, 吉林长春 130012
  2.吉林农业大学信息技术学院, 吉林长春 130118
  3.空军航空大学航空作战勤务学院, 吉林长春 130022
- 作者简介：
  
  [ "张丽娟(1978-), 女, 吉林梅河口人, 博士, 教授, 2015年于长春理工大学获得博士学位, 主要从事计算机视觉及光学图像处理等方面研究。E-mail:zhanglijuan@ccut.edu.cn" ]
  [ "李东明(1979-), 男, 吉林公主岭人, 博士, 教授, 2021年于长春理工大学获得博士学位, 主要从事计算机视觉、图像处理及光学检测等方面研究。E-mail: ldm0214@163.com" ]
- 基金信息：
  
  国家自然基金(61806024);吉林省教育厅科学研究项目(JJKH20181041KJ);吉林省教育厅科学研究项目(JJKH20200678KJ);吉林省教育厅科学研究项目(JJKH20210747KJ)
- DOI：10.37188/CJLCD.2020-0338
  中图分类号： TP391.4
- 收稿日期：2020-12-07，
  
  修回日期：2021-01-27，
  
  纸质出版日期：2021-09
- 稿件说明：
移动端阅览
张丽娟, 章润, 李东明, 等. 区域生长全卷积神经网络交互分割肝脏CT图像[J]. 液晶与显示, 2021,36(9):1294-1304.

Li-juan ZHANG, Run ZHANG, Dong-ming LI, et al. Region-growing fully convolutional neural network interactive segmentation of liver CT images[J]. Chinese journal of liquid crystals and displays, 2021, 36(9): 1294-1304.
张丽娟, 章润, 李东明, 等. 区域生长全卷积神经网络交互分割肝脏CT图像[J]. 液晶与显示, 2021,36(9):1294-1304. DOI： 10.37188/CJLCD.2020-0338.

Li-juan ZHANG, Run ZHANG, Dong-ming LI, et al. Region-growing fully convolutional neural network interactive segmentation of liver CT images[J]. Chinese journal of liquid crystals and displays, 2021, 36(9): 1294-1304. DOI： 10.37188/CJLCD.2020-0338.

摘要

由于医疗图像质量差、对比度低、患者之间差异大导致全自动分割方法很难获得足够准确、鲁棒的结果。为了解决全自动分割方法的局限性，本文提出一种基于神经网络改进的区域生长法，并与全卷积神经网络相结合对肝脏CT图像进行交互式分割。首先对图像进行预处理，突出待分割肝脏区域；接着计算像素在不同边缘检测算子下的梯度值作为该像素的特征，形成像素特征向量训练网络：该网络以一对像素特征向量为输入，以两像素的关联度系数为输出；然后将训练好的神经网络模型作为区域生长算法的生长准则，手动交互选取一点产生分割结果；最后将分割结果作为原图的交互信息和原图灰度通道连接在一起一同输入全卷积神经网络。实验结果表明：平均Dice系数达到96.69%，像素准确率达到99.62%，平均交并比达到96.65%。不同的腹部CT图像序列中肝脏的分割结果表明，该方法能精确提取肝脏区域，满足临床应用的需求。

Abstract

Due to poor medical image quality

low contrast

large differences between patients

it is difficult for fully automatic segmentation methods to obtain sufficiently accurate and robust results. In order to solve the limitations of the automatic segmentation method

this paper proposes an improved region growing method based on neural network

and combined with the fully convolutional neural network to interactively segment liver CT images. Firstly

the image is preprocessed to highlight the liver area to be segmented. Then

the gradient value of the pixel under different edge detection operators is calculated as the feature of the pixel to form a pixel feature vector training network. The network takes a pair of pixel feature vectors as input and the correlation coefficient of two pixels as output. Then

the trained neural network model is used as the growth criterion of the region growing algorithm

and a point is manually selected interactively to generate the segmentation result. Finally

the segmentation result is connected as the interactive information of the original image and the gray channel of the original image and input into the full convolutional neural network. The experimental results show that the average Dice coefficient reaches 96.69%

the pixel accuracy rate reaches 99.62%

and the average intersection ratio reaches 96.65%. The results of liver segmentation in different abdominal CT image sequences show that this method can accurately extract liver regions and meet the needs of clinical applications.

关键词

Keywords

references

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