Content
In its 4th edition, the Diabetic Foot Ulcer Challenge (DFUC) 2024 focuses on the task of Diabetic Foot Ulcer (DFU) segmentation by involving aspects of self-supervised learning as contrast to the DFUC 2022. This paper provides information on the challenge background, outlines details on a synthetic extension of the DFU dataset, elaborates rules for participation, and reports performance of two vastly different baseline methods that highlight the potential diversity of welcome approaches. It further provides a condensed summary on final challenge results and methods employed by participating teams. A discussion puts the different approaches and outcomes into perspective, considering their creativity and applicability on a larger scale. Eventually, a retrospective on the DFUC 2024 finds the given task to be considerably challenging, inspiring diverse contributions. Even though accurate DFU segmentation currently seems out of reach when involving a higher level of self-supervision, respective contributions demonstrate promising kick-off strategies for scenarios in which unlabeled data needs to be harnessed.