Abstract
Diabetic foot syndrome is one of the chronic diabetic complications that present itself as foot ulceration, which in many cases leads to limb amputation. Moreover, it is linked to the high percentage of post-amputation mortality within the period of five years. Thus it is crucial to diagnose and plan careful treatment properly in the early stages. Diagnosis is often time-consuming and requires a skilled clinician who can differentiate between similarities between diabetes-related and other types of ulcers by evaluating their morphology and location. To mitigate diagnostic issues, we propose an improved version of the nnU-Net architecture with residual short skip connections in the encoder part and additional mixup augmentation as the preprocessing step. The obtained results on the DFUC2022 challenge dataset show that our improvements can boost overall performance for ulcer segmentation tasks, even in scenarios where targeted structures are heterogeneous and under high imbalance conditions in the evaluated dataset. With our approach we achieved 9th place with a Dice score of 0.6975.
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Acknowledgements
This work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences under contract VEGA 1/0327/20.
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Hresko, D.J., Vereb, J., Krigovsky, V., Gayova, M., Drotar, P. (2023). Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation. In: Yap, M.H., Kendrick, C., Cassidy, B. (eds) Diabetic Foot Ulcers Grand Challenge. DFUC 2022. Lecture Notes in Computer Science, vol 13797. Springer, Cham. https://doi.org/10.1007/978-3-031-26354-5_8
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