Abstract
Diabetic foot ulcer is a serious manifestation of lesions on the diabetic foot that requires close monitoring and management. The research at hand investigates an approach on segmentation of diabetic foot ulcer area, conducted as part of the Diabetic Foot Ulcer Challenge (DFUC) 2022. We use OCRNet as the baseline for segmentation and a powerful ConvNeXt network was adopted as the backbone. To obtain better results, a boundary loss was introduced to further constrain the boundary of segmentation. In addition, gamma correction was used in the inference stage in order to reduce the difference in luminance between the training, validation and test sets. Our method won 2nd place in the DFUC2022 with a Dice score of 72.80%. Source code is available at: DFUC2022SegmentationOcrnet.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Baig, M.S., et al.: An overview of diabetic foot ulcers and associated problems with special emphasis on treatments with antimicrobials. Life 12(7), 1054 (2022)
Bouallal, D., Douzi, H., Harba, R.: Diabetic foot thermal image segmentation using double encoder-ResUnet (DE-ResUnet). J. Med. Eng. Technol. 1–15 (2022). Taylor & Francis
Cassidy, B., et al.: The DFUC 2020 dataset: analysis towards diabetic foot ulcer detection. touchREVIEWS Endocrinol. 17, 5–11 (2021). https://www.touchendocrinology.com/diabetes/journal-articles/the-dfuc-2020-dataset-analysis-towards-diabetic-foot-ulcer-detection/1
Contributors, M.: MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark (2020). https://github.com/open-mmlab/mmsegmentation
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S., Spragg, J.: Fully convolutional networks for diabetic foot ulcer segmentation. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 618–623. IEEE (2017)
Jawahar, M., Anbarasi, L.J., Jasmine, S.G., Narendra, M.: Diabetic foot ulcer segmentation using color space models. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 742–747. IEEE (2020)
Kendrick, C., et al.: Translating clinical delineation of diabetic foot ulcers into machine interpretable segmentation. arXiv preprint arXiv:2204.11618 (2022)
Li, J., Wang, C., Huang, B., Zhou, Z.: ConvNeXt-backbone HoVerNet for nuclei segmentation and classification. arXiv preprint arXiv:2202.13560 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Mahbod, A., Ecker, R., Ellinger, I.: Automatic foot ulcer segmentation using an ensemble of convolutional neural networks. arXiv preprint arXiv:2109.01408 (2021)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Yap, M.H., et al.: Diabetic foot ulcers grand challenge 2022 (2021). https://doi.org/10.5281/zenodo.6389665
Yuan, Y., Chen, X., Chen, X., Wang, J.: Segmentation transformer: object-contextual representations for semantic segmentation. arXiv preprint arXiv:1909.11065 (2019)
Yuan, Y., Chen, X., Wang, J.: Object-contextual representations for semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 173–190. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_11
Acknowledgement
This study was supported by National Key Research and Development Program of China (2020YFB1711500, 2020YFB1711503), the 1\(\cdot \)3\(\cdot \)5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYYC21004).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yi, H. et al. (2023). OCRNet for Diabetic Foot Ulcer Segmentation Combined with Edge Loss. 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_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-26354-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26353-8
Online ISBN: 978-3-031-26354-5
eBook Packages: Computer ScienceComputer Science (R0)
