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. The ability to estimate the area of ulcer regions and its pathology are important aspects in DFU management [1]. Manual delineation of ulcers regions are very time-consuming and challenging for podiatrists. 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. With joint effort from the lead scientists of the UK, US, India and New Zealand, two challenges on DFU detection [2,3] and DFU classification [6,7] were successfully conducted. Rather than focusing on single task, this challenge will focus on DFU semantic segmentation, which will automate the segmentation of the ulcer region and recognise the pathology of the ulcer, simultaneously. This event will solicit original works in DFU and promote interactions between interdisciplinary researchers. References [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., 2021. The DFUC 2020 dataset: Analysis towards diabetic foot ulcer detection. touchREVIEWS in Endocrinology, 17(1), p.5. [3] Yap, M.H. et al., 2020. Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation. Computers in Biology and Medicine, p.104596. [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 [6] Yap, M.H., Cassidy, B., Pappachan, J.M., O'Shea, C., Gillespie, D. and Reeves, N. (2021), "Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers," 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2021, pp. 1-4, doi: 10.1109/BHI50953.2021.9508563. [7] Cassidy, B., Kendrick, C., Reeves, N.D., Pappachan, J.M., O'Shea, C., Armstrong, D.G. and Yap, M.H. (2021). Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary. arXiv preprint arXiv:2111.10376.