首页|基于全景病理图像的肿瘤钙化智能评估方法

基于全景病理图像的肿瘤钙化智能评估方法

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肿瘤钙化是指在肿瘤组织中的钙盐沉积现象,在病理图像中,肿瘤钙化区域的占比分析对于疾病进展监测、治疗效果跟踪以及精准医疗如评估肿瘤生长和放化疗效果具有重要意义.全景病理图像相较于传统病理图像具有高质量图像保存、远程访问和共享、多维数据分析等优势,结合人工智能算法自动化的图像分割、特征提取和数据计算使得病理评估过程更加高效、精准.病理图像中的肿瘤钙化区域形态多样且分布离散,医生通常需要人工估算钙化区域占比,耗时且不精准.为解决这一问题,研究了一种基于全景病理图像的肿瘤钙化智能评估系统,该系统采用融合注意力模块的ECR-UNet网络实现钙化区域的精准分割,并采用边缘检测技术分割病理图像的外轮廓,再分别计算出二者面积后得到钙化区域占比.改进后的网络在测试集上展现出较好的分割性能,其Dice系数、准确率和精确度分别为 89.13%、98.94%和90.81%.将分割后计算得到的病理图像钙化区域面积、外轮廓面积和钙化区域占比与医生的金标准进行对比,结果显示,平均精度分别为92.25%、99.05%和91.76%.该方法为肿瘤钙化区域的检测提供了有效工具,可辅助病理医生进行肿瘤钙化诊断工作.
Intelligent Evaluation of Tumor Calcification Areas Based on Whole Slide Images
Tumor calcification refers to the phenomenon of calcium salt deposition in tumor tissues.In pathological sections,the analysis of the proportion of calcified areas is of great significance for the benign and malignant classification of tumors,monitoring of disease progression,tracking of treatment effects,and precision medicine such as surgery and radiotherapy.Compared with traditional pathological sections,fully digital pathological sections have advantages such as high-quality image preservation,remote access and sharing,and multidimensional data analysis.The integration of artificial intelligence algorithms for automated image segmentation,feature extraction,and data computation makes pathological assessment more efficient and accurate.The areas of tumor calcification areas in pathological sections are diverse and distributed discretely.Doctors typically need to manually estimate the proportion of calcified areas,which is time-consuming and imprecise.To address this issue,this study investigated an intelligent assessment system for tumor calcification areas based on fully digital pathological sections.The system utilizes the ECR-UNet network that integrates the attention modules to achieve precise segmentation of calcification areas.Edge detection technology is employed to segment the outer sections of pathological sections.The areas of both regions are then calculated separately to determine the proportion of calcified areas.The improved network demonstrates good segmentation performance on the test set,with the Dice coefficient,accuracy,and precision reaching 89.13%,98.94%,and 90.81%,respectively.A comparison with the gold standard set by doctors for segmented calcified areas,outer contour areas,and the proportion of calcified areas on pathological sections reveals average accuracies of 92.25%,99.05%,and 91.76%,respectively.This method provides an effective tool for the intelligent assessment of tumor calcification areas,assisting pathologists in tumor calcification diagnosis.

medical image processingtumor calcificationwhole slide imagecomputer-aided diagnosis and treatmentdigital pathology slide scanner

万真真、李昊成、施宁、刘雨薇、刘芳

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河北大学电子信息工程学院河北省数字医疗工程重点实验室,河北 保定 071002

首都医科大学附属北京儿童医院保定医院病理科,河北 保定 071002

医学图像处理 肿瘤钙化 全景病理图像 计算机辅助诊疗 数字病理切片扫描仪

2024

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子:1.153
ISSN:1006-4125
年,卷(期):2024.61(22)