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Region-growing fully convolutional neural network interactive segmentation of liver CT images
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 9, Pages: 1294-1304(2021)
- 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：
- 基金信息：Supported by the National Natural Science Foundation of China(61806024);Science and Technology Project of Department of Education of JiLin Province(JJKH20181041KJ);Science and Technology Project of Department of Education of JiLin Province(JJKH20200678KJ);Science and Technology Project of Department of Education of JiLin Province(JJKH20210747KJ)
DOI：10.37188/CJLCD.2020-0338
CLC： TP391.4Received：07 December 2020，
Revised：27 January 2021，
Published：2021-09
- 稿件说明：
移动端阅览
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.
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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