首个真菌/细菌性麦克尔森病组织图像数据库，助力病理自动诊断。
MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis
- 构建142例患者864张显微图像数据库，标注颗粒位置与形态
- 包含真菌与细菌性麦克尔森病图像，支持分类与分割任务
- 为资源匮乏地区提供自动化病理分析工具，适合医学影像研究者
麦克尔森病是一种常见于热带和亚热带地区的慢性炎症性疾病，可导致严重残疾与社会歧视。该病分为真菌性（eumycetoma）和细菌性（actinomycetoma）两类，治疗方案依赖于准确识别致病微生物。现有鉴定方法包括分子、细胞学、组织病理学及培养技术，其中组织病理学在流行区最具优势，但需依赖专家判读，而农村地区缺乏专业病理科医生。数字病理与自动化图像分析技术为此提供可能解决方案。本文介绍首个用于麦克尔森病组织图像自动检测与分类的综合性数据集，涵盖142名患者的864张显微图像，每张图像均附有二值掩码标注颗粒区域，支持检测与分割任务。数据集完整记录了从物种分布、患者采样到染色与成像的全流程技术规范。
原文摘要 · Abstract (English)
Mycetoma is a chronic and neglected inflammatory disease prevalent in tropical and subtropical regions. It can lead to severe disability and social stigma. The disease is classified into two types based on the causative microorganisms: eumycetoma (fungal) and actinomycetoma (bacterial). Effective treatment strategies depend on accurately identifying the causative agents. Current identification methods include molecular, cytological, and histopathological techniques, as well as grain culturing. Among these, histopathological techniques are considered optimal for use in endemic areas, but they require expert pathologists for accurate identification, which can be challenging in rural areas lacking such expertise. The advent of digital pathology and automated image analysis algorithms offers a potential solution. This report introduces a novel dataset designed for the automated detection and classification of mycetoma using histopathological images. It includes the first database of microscopic images of mycetoma tissue, detailing the entire pipeline from species distribution and patient sampling to acquisition protocols through histological procedures. The dataset consists of images from 142 patients, totalling 864 images, each annotated with binary masks indicating the presence of grains, facilitating both detection and segmentation tasks.
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