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Related Experiment Video

Updated: Jun 29, 2025

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Published on: March 3, 2023

Diabetic foot ulcers segmentation challenge report: Benchmark and analysis.

Moi Hoon Yap¹, Bill Cassidy², Michal Byra³

¹Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom; Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, United Kingdom.

Medical Image Analysis

|April 3, 2024

Summary

Diabetic foot ulcer segmentation is crucial for monitoring healing. A challenge provided 2000 images, leading to automated systems that achieved a 0.7287 Dice score for accurate wound measurement.

Keywords:

Convolutional neural networks Deep learning Diabetic foot ulcers Metrics Segmentation

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Last Updated: Jun 29, 2025

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Area of Science:

Medical image analysis
Computer-assisted diagnosis
Diabetic wound care

Background:

Accurate segmentation of diabetic foot ulcers aids in quantitative wound measurement and healing prediction.
Manual segmentation is time-consuming and laborious, hindering progress.
Deep learning methods show promise but require large datasets and consensus on optimal approaches.

Purpose of the Study:

To stimulate progress in diabetic foot ulcer segmentation.
To establish a benchmark for automated segmentation methods.
To address the lack of large-scale, manually delineated datasets.

Main Methods:

A challenge dataset of 2000 diabetic foot ulcer images with ground truth masks was released.
26 teams developed automated systems for ulcer segmentation.
Systems were evaluated using the Dice similarity coefficient on a private test set.

Main Results:

The winning team achieved a Dice similarity coefficient of 0.7287 for diabetic foot ulcer segmentation.
The challenge provided a benchmark for evaluating automated segmentation algorithms.
The competition fostered development of advanced segmentation techniques.

Conclusions:

Automated diabetic foot ulcer segmentation is feasible and advancing.
The challenge dataset and leaderboard serve as valuable resources for research.
Further development is needed to improve segmentation accuracy and clinical utility.