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Diabetic Foot Ulcers Grand Challenge 2022

Moi Hoon Yap; Neil Reeves; Andrew Boulton; Satyan Rajbhandari; David Armstrong; Arun G. Maiya; Bijan Najafi; Eibe Frank; Justina Wu

Diabetes is a global epidemic affecting approximately 425 million people. This figure is expected to rise to 629 million people by 2045. 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 focused 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 [2-4]. To this end, the collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHS Foundation Trust has created an international repository of up to 11,000 DFU images for the purpose of supporting more advanced methods of DFU research. Analysis of ulcer regions is an important aspect in DFU management [1]. Manual delineation of ulcers regions are very time-consuming and challenging for podiatrists. With joint effort from the lead scientists of the UK, US, India and New Zealand, this challenge will solicit original works in automated DFU segmentation and promote interactions between researchers and
interdisciplinary collaborations.


[1] Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S. and Spragg, J., 2017, October. Fully convolutional networks for diabetic foot ulcer segmentation. In 2017 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 618-623). IEEE.
[2] Cassidy B. et al., 2020. DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection. arXiv preprint arXiv:2004.11853. 2020 Apr 24.
[3] Yap, M.H. et al., 2020. Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation. arXiv preprint arXiv:2010.03341.
[4] 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
[5] Maier-Hein et al. (2020) BIAS: Transparent reporting of biomedical image analysis challenges. Medical Image Analysis, 101796. doi: https://doi.org/10.1016/j.media.2020.101796

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