基于逐像素点深度卷积网络分割模型的上皮和间质组织分割
发布时间：2018-10-12 13:35
【摘要】：上皮和间质组织是乳腺组织病理图像中最基本的两种组织,约80%的乳腺肿瘤起源于乳腺上皮组织.为了构建基于乳腺组织病理图像分析的计算机辅助诊断系统和分析肿瘤微环境,上皮和间质组织的自动分割是重要的前提条件.本文构建一种基于逐像素点深度卷积网络(CN-PI)模型的上皮和间质组织的自动分割方法.1)以病理医生标注的两类区域边界附近具有类信息为标签的像素点为中心,构建包含该像素点上下文信息的正方形图像块的训练集.2)以每个正方形图像块包含的像素的彩色灰度值作为特征,以这些图像块中心像素类信息为标签训练CN模型.在测试阶段,在待分割的组织病理图像上逐像素点地取包含每个中心像素点上下文信息的正方形图像块,并输入到预先训练好的CN网络模型,以预测该图像块中心像素点的类信息.3)以每个图像块中心像素为基础,逐像素地遍历图像中的每一个像素,将预测结果作为该图像块中心像素点类信息的预测标签,实现对整幅图像的逐像素分割.实验表明,本文提出的CN-PI模型的性能比基于图像块分割的CN网络(CN-PA)模型表现出了更优越的性能.
[Abstract]:Epithelium and mesenchymal tissue are the two most basic tissues in breast histopathological images. About 80% of breast tumors originate from mammary epithelial tissue. In order to construct a computer-aided diagnosis system based on breast pathological image analysis and analyze tumor microenvironment, automatic segmentation of epithelial and interstitial tissues is an important prerequisite. In this paper, an automatic segmentation method of epithelial and interstitial tissue based on pixel by pixel depth convolution network (CN-PI) model is proposed. A training set of square image blocks containing the context information of the pixel is constructed. 2) the color gray value of the pixels contained in each square image block is used as the feature, and the central pixel class information of these blocks is used as the label to train the CN model. In the test phase, the square image blocks containing the context information of each central pixel point are selected from the histopathological image to be segmented, and input to the pre-trained CN network model. Based on the class information of predicting the central pixel of the image block. 3) based on the central pixel of each image block, every pixel in the image is traversed pixel by pixel, and the prediction result is used as the prediction label of the pixel class information in the center of the image block. The pixel-by-pixel segmentation of the whole image is realized. Experimental results show that the performance of the proposed CN-PI model is better than that of the CN neural network (CN-PA) model based on image block segmentation.
【作者单位】： 南京信息工程大学江苏省大数据分析技术重点实验室;武汉大学中南医院肿瘤科肿瘤生物学行为湖北省重点实验室 湖北省肿瘤医学临床研究中心;
【基金】：国家自然科学基金(61771249,61273259) 江苏省“六大人才高峰”高层次人才项目资助计划(2013-XXRJ-019) 江苏省自然科学基金(BK20141482) 江苏创新创业团队人才计划(JS201526)资助~~
【分类号】：R737.9;TP391.41
，
本文编号：2266316
[Abstract]:Epithelium and mesenchymal tissue are the two most basic tissues in breast histopathological images. About 80% of breast tumors originate from mammary epithelial tissue. In order to construct a computer-aided diagnosis system based on breast pathological image analysis and analyze tumor microenvironment, automatic segmentation of epithelial and interstitial tissues is an important prerequisite. In this paper, an automatic segmentation method of epithelial and interstitial tissue based on pixel by pixel depth convolution network (CN-PI) model is proposed. A training set of square image blocks containing the context information of the pixel is constructed. 2) the color gray value of the pixels contained in each square image block is used as the feature, and the central pixel class information of these blocks is used as the label to train the CN model. In the test phase, the square image blocks containing the context information of each central pixel point are selected from the histopathological image to be segmented, and input to the pre-trained CN network model. Based on the class information of predicting the central pixel of the image block. 3) based on the central pixel of each image block, every pixel in the image is traversed pixel by pixel, and the prediction result is used as the prediction label of the pixel class information in the center of the image block. The pixel-by-pixel segmentation of the whole image is realized. Experimental results show that the performance of the proposed CN-PI model is better than that of the CN neural network (CN-PA) model based on image block segmentation.
【作者单位】： 南京信息工程大学江苏省大数据分析技术重点实验室;武汉大学中南医院肿瘤科肿瘤生物学行为湖北省重点实验室 湖北省肿瘤医学临床研究中心;
【基金】：国家自然科学基金(61771249,61273259) 江苏省“六大人才高峰”高层次人才项目资助计划(2013-XXRJ-019) 江苏省自然科学基金(BK20141482) 江苏创新创业团队人才计划(JS201526)资助~~
【分类号】：R737.9;TP391.41
，
本文编号：2266316
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