MyData：用于组织病理学分析的真菌肿组织显微图像综合数据库
MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis
AI总结：
本文提出MyData数据集，包含142名患者共864张带二值掩码标注的真菌肿组织显微图像，用于自动化检测与分类真菌肿，并详细记录了从采样到图像获取的完整流程。
摘要：
真菌肿是一种在热带和亚热带地区流行的慢性、被忽视的炎症性疾病，可导致严重残疾和社会污名化。根据致病微生物的不同，该病分为两类：真菌性足菌肿（eumycetoma，真菌引起）和放线菌性足菌肿（actinomycetoma，细菌引起）。有效的治疗策略依赖于准确识别致病因子。目前的识别方法包括分子学、细胞学和组织病理学技术以及颗粒培养。其中，组织病理学技术被认为最适合在流行地区使用，但需要病理学专家进行准确识别，这在缺乏此类专业知识的农村地区可能具有挑战性。数字病理学和自动化图像分析算法的出现提供了一种潜在的解决方案。本报告介绍了一个新颖的数据集，旨在利用组织病理学图像实现真菌肿的自动化检测和分类。该数据集包含首个真菌肿组织显微图像数据库，详细描述了从物种分布和患者采样到通过组织学程序获取图像的完整流程。该数据集包含来自142名患者的图像，共计864张图像，每张图像均标注有指示颗粒存在的二值掩码，可同时支持检测和分割任务。
英文摘要：
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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