MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis

TL;DR

This paper introduces MyData, the first comprehensive database of microscopic mycetoma tissue images, to support automated detection and classification, aiding diagnosis especially in resource-limited settings.

Contribution

It provides a detailed, annotated dataset of mycetoma histopathological images, enabling development of automated diagnostic tools for the first time.

Findings

First database of mycetoma tissue images with annotations

Supports detection and segmentation tasks

Facilitates development of automated diagnosis methods

Abstract

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.