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AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge

Hyam Omar Ali, Sahar Alhesseen, Lamis Elkhair, Adrian Galdran, Ming Feng, Zhixiang Xiong, Zengming Lin, Kele Xu, Liang Hu, Benjamin Keel, Oliver Mills, James Battye, Akshay Kumar, Asra Aslam, Prasad Dutande, Ujjwal Baid, Bhakti Baheti, Suhas Gajre, Aravind Shrenivas Murali, Eung-Joo Lee, Ahmed Fahal, Rachid Jennane

TL;DR

The paper presents the Mycetoma MicroImage Challenge (mAIcetoma) at MICCAI 2024, which aims to automate grain segmentation and mycetoma type classification in histopathology images using the MyData dataset. It reports diverse finalist approaches, achieves strong segmentation Dice scores and high classification accuracies, and discusses evaluation design, dataset limitations, and lessons learned for future work. The study demonstrates the feasibility of AI-assisted mycetoma diagnosis, with implications for diagnostics in low-resource settings and pathways toward clinical integration and multi-centre validation. Public data availability supports ongoing development and benchmarking of automated histopathology tools for mycetoma.

Abstract

Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in low-resource settings where expert pathologists are limited. To address this challenge, this paper presents an overview of the Mycetoma MicroImage: Detect and Classify Challenge (mAIcetoma) which was organized to advance mycetoma diagnosis through AI solutions. mAIcetoma focused on developing automated models for segmenting mycetoma grains and classifying mycetoma types from histopathological images. The challenge attracted the attention of several teams worldwide to participate and five finalist teams fulfilled the challenge objectives. The teams proposed various deep learning architectures for the ultimate goal of this challenge. Mycetoma database (MyData) was provided to participants as a standardized dataset to run the proposed models. Those models were evaluated using evaluation metrics. Results showed that all the models achieved high segmentation accuracy, emphasizing the necessitate of grain detection as a critical step in mycetoma diagnosis. In addition, the top-performing models show a significant performance in classifying mycetoma types.

AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge

TL;DR

The paper presents the Mycetoma MicroImage Challenge (mAIcetoma) at MICCAI 2024, which aims to automate grain segmentation and mycetoma type classification in histopathology images using the MyData dataset. It reports diverse finalist approaches, achieves strong segmentation Dice scores and high classification accuracies, and discusses evaluation design, dataset limitations, and lessons learned for future work. The study demonstrates the feasibility of AI-assisted mycetoma diagnosis, with implications for diagnostics in low-resource settings and pathways toward clinical integration and multi-centre validation. Public data availability supports ongoing development and benchmarking of automated histopathology tools for mycetoma.

Abstract

Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in low-resource settings where expert pathologists are limited. To address this challenge, this paper presents an overview of the Mycetoma MicroImage: Detect and Classify Challenge (mAIcetoma) which was organized to advance mycetoma diagnosis through AI solutions. mAIcetoma focused on developing automated models for segmenting mycetoma grains and classifying mycetoma types from histopathological images. The challenge attracted the attention of several teams worldwide to participate and five finalist teams fulfilled the challenge objectives. The teams proposed various deep learning architectures for the ultimate goal of this challenge. Mycetoma database (MyData) was provided to participants as a standardized dataset to run the proposed models. Those models were evaluated using evaluation metrics. Results showed that all the models achieved high segmentation accuracy, emphasizing the necessitate of grain detection as a critical step in mycetoma diagnosis. In addition, the top-performing models show a significant performance in classifying mycetoma types.
Paper Structure (14 sections, 2 equations, 2 figures, 4 tables)

This paper contains 14 sections, 2 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Challenge Design
  3. Challenge statistics
  4. Challenge Workflow and Timeline
  5. Dataset Description
  6. Participating Teams and Methods
  7. Methods
  8. Evaluation method
  9. Ranking Scheme
  10. Results
  11. Discussion
  12. Key Lessons Learned
  13. General Discussion
  14. Conclusion

Figures (2)

  • Figure 1: Representative histopathological images of mycetoma. The first row shows examples of the two classes: Actinomycetoma (AM) and Eumycetoma (EM). The second row presents the corresponding segmentation masks highlighting the mycetoma grains within the tissue samples.
  • Figure 2: The distribution of teams that registered for the challenge around the world