This challenge is now closed. Thank you to all who participated. Please continue to use our Live Leaderboard to evaluate your test results.¶

For DFUC2020 Challenge results, please visit:  Moi Hoon Yap et al. (2021), Deep learning in diabetic foot ulcers detection: A comprehensive evaluation, Computers in Biology and Medicine, Volume 135, 2021, 104596, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2021.104596.¶

Diabetes is a global epidemic affecting approximately 425 million people. This figure is expected to rise to 629 million people by 2045 [1]. Diabetic Foot Ulcers (DFU) are a serious condition that frequently results from the disease. The rapid rise of the condition over the last few decades is a major challenge for healthcare systems around the world. Cases of DFU frequently lead to more serious conditions, such as infection and ischaemia, that can significantly prolong treatment, and often result in limb amputation, with more serious cases leading to death. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focussed on the creation of detection algorithms that could be used as part of a mobile app that patients could use themselves (or a carer/partner) to monitor their condition and to detect the appearance of DFU [2][3]. To this end, the collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospital and the Manchester University NHS Foundation Trust has created a repository of 4500 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. With joint effort from the lead scientists of the UK, US, India and New Zealand, this challenge will solicit the original works in DFU, and promote interactions between researchers and interdisciplinary collaborations.

References¶

[1] Cho, N., Shaw, J.E., Karuranga, S., Huang, Y., da Rocha Fernandes, J.D. et al. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 138(2018): 271-281.
[2] Goyal, M., Reeves, N., Rajbhandari, S., & Yap, M. H. (2019). Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices. IEEE Journal of Biomedical and Health Informatics. 23(4), 1730- 1741, doi:10.1109/JBHI.2018.2868656
[3] Yap, M. H., Chatwin, K. E., Ng, C. C., Abbott, C. A., Bowling, F. L., Rajbhandari, S., . . . Reeves, N. D. (2018). A New Mobile Application for Standardizing Diabetic Foot Images. Journal of Diabetes Science and Technology, 12(1), 169-173. doi:10.1177/1932296817713761

DFUC is hosted by MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention.

https://www.miccai2020.org/en/

The prize for the winning team of DFUC2020 is an NVIDIA Titan RTX GPU. MMU would like to thank
NVIDIA for their sponsorship of DFUC2020.