基于图特征的组织病理学图像分析方法的最新发展情况与展望

doi:10.11871/jfdc.issn.2096-742X.2024.02.010

数据与计算发展前沿 ›› 2024, Vol. 6 ›› Issue (2): 101-116.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.02.010

doi: 10.11871/jfdc.issn.2096-742X.2024.02.010

• 专刊:智慧医疗前沿与进展（上） • 上一篇下一篇

基于图特征的组织病理学图像分析方法的最新发展情况与展望

何睿琳¹(),杨欣怡¹,孙洪赞²,李晨^1,^*()

1.东北大学，医学与生物信息工程学院，辽宁沈阳 110819
2.中国医科大学附属盛京医院，辽宁沈阳 110004

收稿日期:2023-11-08 出版日期:2024-04-20 发布日期:2024-04-26
通讯作者: *李晨（E-mail: lichen@bmie.neu.edu.cn）
作者简介:何睿琳，东北大学医学与生物信息工程学院生物医学工程专业本科生，主要研究方向为组织病理学图像分析，医学影像处理，计算机视觉领域。
本文中负责文献整理分析和论文的撰写
HE Ruilin is an undergraduate student majoring in Biomedical Engineering at the College of Medicine and Biological Information Engineering, Northeastern University, China. Her main research interests include histopathological image analysis, medical image processing, and computer vision.
In this paper, she is responsible for literature review and analysis, as well as writing papers.
E-mail: ruilin12468172@163.com|李晨，东北大学医学与生物信息工程学院，副教授，博士生导师。医学图像分析方向，涉及模式识别、人工智能、机器视觉等具体技术。
本文中负责本文框架确定、指导、文献筛选、论文撰写及校对。
LI Chen is an Associate Professor at the College of Medicine and Biological Information Engineering, Northeastern University, China. His research interests include pattern recognition, artificial intelligence, and machine vision.
In this paper, he is responsible for determining the framework, providing guidance, conducting literature screening, writing the paper, and proofreading.
E-mail: lichen@bmie.neu.edu.cn
基金资助:
国家自然科学基金重点项目(82220108007)

The Latest Development and Prospects of Histopathological Image Analysis Methods Based on Graph Features

HE Ruilin¹(),YANG Xinyi¹,SUN Hongzan²,LI Chen^1,^*()

1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110819, China
2. Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China

Received:2023-11-08 Online:2024-04-20 Published:2024-04-26

摘要/Abstract

摘要：

【目的】本文旨在综述最近五年人工智能在辅助组织病理学分析方面的研究进展，主要是图特征方法的应用、当前面临的问题以及未来的挑战。【方法】文章回顾了图论在组织病理学图像分析中的应用，包括图像分割、检测和分类。探讨了图像拓扑结构特征提取的各种图构建算法，例如经典的最小生成树算法及其衍生创新算法等，并分析了图卷积神经网络等网络结构的性能。【结果】通过结构图提取的图特征能够有效表示组织病理学图像中的拓扑信息，有助于实现精确的肿瘤分割、检测以及分类、分级等任务。此外，图特征方法综合全局与局部特征，提供了一种系统化的分析方式，促进了对复杂病理学图像的理解。【结论】图特征与先进的机器学习技术相结合在组织病理学图像分析中展现出强大的潜力，未来这些方法将被优化以提高临床诊断的准确性和效率。

关键词: 组织病理学图像, 图特征, 人工智能, 机器学习, 肿瘤辅助诊断

Abstract:

[Objective] This article aims to review the research progress of artificial intelligence in assisting histopathology analysis in the past five years, mainly focusing on the application of graph feature methods, current problems, and future challenges. [Methods] The article reviews the application of graph theory in histopathological image analysis, including image segmentation, detection, and classification, explores various graph construction algorithms for feature extraction of image topological structures, such as the classic minimum spanning tree algorithm and its derivative innovative algorithms, and analyzes the performance of network structures such as graph convolutional neural networks. [Results] The graph features extracted through structural maps can effectively represent topological information in histopathological images, which helps to achieve accurate tumor segmentation, detection, classification, and cancer grading tasks. In addition, the graph feature method provides a systematic analysis approach by considering global and local features, promoting the understanding of complex tissue pathology images. [Conclusions] The combination of graph features and advanced machine learning technologies has shown strong potential in histopathological image analysis. In the future, these methods will be optimized to improve the accuracy and efficiency of clinical diagnosis.

Key words: histopathological image, graph feature, artificial intelligence, machine learning, tumor assisted diagnosis

何睿琳, 杨欣怡, 孙洪赞, 李晨. 基于图特征的组织病理学图像分析方法的最新发展情况与展望[J]. 数据与计算发展前沿, 2024, 6(2): 101-116.

HE Ruilin, YANG Xinyi, SUN Hongzan, LI Chen. The Latest Development and Prospects of Histopathological Image Analysis Methods Based on Graph Features[J]. Frontiers of Data and Computing, 2024, 6(2): 101-116, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.02.010.

图/表 8

图1

图2

图3

图4

表1

表2

图5

表3

图方法在图像分类中的应用汇总"

任务	年份	文献	方法	标签	结果评估
二分类	2021	[37]	结合CNN特征提取和GCN的图像关联学习	有监督	分类准确度：98.37%
	2022	[38]	复合扩张的主干网络(composite dilated backbone network (CDBN))	有监督	分类准确度：94.00%
	2023	[39]	CRCCN-Net分类网络模型	有监督	分类准确度：96.26%
	2023	[40]	自适应、可扩展的图卷积网络GraphLSurv	有监督	concordance-index:0.683,比其他方法提高了3.4%
	2023	[42]	GARL-Net网络，深度神经网络DNN	有监督	分类准确度：99.00%
	2021	[43]	支持向量机 (SVM) 和 K-Nearest Neighbour (KNN) 算法	有监督	分类准确度：98.00%
	2022	[44]	GNN+二次谐波生成显微镜图像的胶原纤维形态学特征	有监督	分类准确度：96.20%
	2023	[45]	最小化细胞图（Minimized Cellular Graph，MCG）	有监督	分类准确度：97.70%
	2021	[47]	手工特征（骨骼特征和晶格特征）绘制	有监督
	2022	[48]	深度学习模型特征和手工特征组合提取最佳的混合特征	有监督	分类准确度：91.00%
	2022	[54]	注意全局上下文图卷积神经网络（AGGCN）	有监督	分类准确度：78.20%
	2022	[55]	深度特征图注意网络（DeepGAT）	有监督	分类准确度：95.10%
	2021	[56]	改进对抗生成网络，NAS-SGAN网络	半监督	分类准确度：98.40%
	2023	[58]	异构图的框架和异构图边属性转换器HEAT	伪标签	分类准确度：99.10%
	2021	[59]	基于空间解析的图卷积网络Patch-GCN	弱监督	分类准确度：93.58%
	2021	[30]	图神经网络（GNN）+ 使用对比损失的自监督训练方法	无监督	分类准确度：86.00%
四分类	2023	[41]	分形GCN网络融合到CNN框架中形成CNN-FGCN模型	有监督	分类准确度：95.77%
五分类	2021	[68]	基于多头注意力的多尺度图网络	有监督	分类准确度：71.00%
四分类	2021	[69]	高效网的迁移学习方法	有监督	分类准确度：98.33%
七分类	2022	[70]	图卷积神经网络	有监督	分类准确度：56.25%
四分类	2022	[72]	集成网络模型CNN-GCN	有监督	分类准确度：94.44%
八分类	2022	[74]	依赖性的轻量级卷积神经网络（DBLCNN）	有监督	分类准确度：99.54%
七分类	2022	[76]	新的层次实体图表示（HACT）和层次学习（HACT-Net）方法	有监督	分类准确度：83.79%
三分类	2022	[77]	层次变换图神经网络（HAT-Net+）	无监督	分类准确度：98.00%
三分类	2022	[79]	最小生成树MST	无监督	区分低中高密度区，并提取图特征
三分类	2023	[80]	相关图注意网络（MLP-GAT）	有监督	分类准确度：79.02%

表3

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基于图特征的组织病理学图像分析方法的最新发展情况与展望

The Latest Development and Prospects of Histopathological Image Analysis Methods Based on Graph Features

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年份	文献	技术	标签	结果评估
2023	[25]	GNN网络，引入ViG和FPN神经网络模块处理视觉信息	有监督	Dice系数：0.757
2022	[26]	先嵌入型残差注意力网络模型	有监督	Dice系数：0.954
2022	[27]	GraphSegNet网络架构，创新型图卷积网络(GCNN)	有监督	Dice系数：0.837
2021	[28]	SegGini模型	弱监督	Dice系数：0.696

任务	年份	文献	技术	结果评估
细胞/细胞核检测	2021	[32]	构建跨编码成对相似性的不同核组件的空间结构图	检测精确度：92.2%
	2023	[33]	基于端到端图的核特征对齐（GNFA）方法	检测精确度：76.2%
	2023	[34]	基于GCN的像素分类器	检测精确度：73.4%
	2023	[35]	基于图的节点分类，利用局部细胞特征和全局组织结构进行上皮细胞检测	F1-score：0.918
病灶区域检测	2023	[30]	基于图的稀疏主成分分析（GS-PCA）网络	检测精确度：95.1%
	2022	[29]	基于位置感知图和深度散列（hash）技术，构造位置感知图（LA-Graphs）	检索精确度：89.5%

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