Abstract
Diabetic foot ulcers are caused by neuropathic and vascular complications of diabetes mellitus. In order to provide a proper diagnosis and treatment, wound care professionals need to extract accurate morphological features from the foot wounds. Using computer-aided systems is a promising approach to extract related morphological features and segment the lesions. We propose a convolution neural network called HarDNet-DFUS by enhancing the backbone and replacing the decoder of HarDNet-MSEG, which was the state-of-the-art network for colonoscopy polyp segmentation in 2021. For the MICCAI 2022 Diabetic Foot Ulcer Segmentation Challenge (DFUC2022), we train HarDNet-DFUS using the DFUC2022 dataset and increase its robustness by means of five-fold cross validation and Test Time Augmentation. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean Dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean Dice and was the first place winner. The code is available on https://github.com/kytimmylai/DFUC2022.
T.-Y. Liao, C.-H. Yang, Y.-W. Lo and K.-Y. Lai—These authors contributed equally to this work.
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References
Cassidy, B., et al.: The DFUC 2020 dataset: analysis towards diabetic foot ulcer detection. touchREVIEWS in Endocrinol. 17, 5–11 (2021). https://doi.org/10.17925/EE.2021.17.1.5. https://www.touchendocrinology.com/diabetes/journal-articles/the-dfuc-2020-dataset-analysis-towards-diabetic-foot-ulcer-detection/1
Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: HarDNet: a low memory traffic network. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3552–3561 (2019). https://doi.org/10.1109/ICCV.2019.00365
Cho, N., et al.: IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diab. Res. Clin. Pract. 138, 271–281 (2018). https://doi.org/10.1016/j.diabres.2018.02.023
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904–1916 (2015). https://doi.org/10.1109/TPAMI.2015.2389824
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745
Huang, C.H., Wu, H.Y., Lin, Y.L.: HarDNet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 Mean Dice and 86 FPS. arXiv preprint arXiv:2101.07172 (2021)
Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.: DoubleU-Net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 558–564 (2020). https://doi.org/10.1109/CBMS49503.2020.00111
Jha, D., Smedsrud, P.H., Riegler, M.A., et al.: ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255 (2019). https://doi.org/10.1109/ISM46123.2019.00049
Kendrick, C., et al.: Translating clinical delineation of diabetic foot ulcers into machine interpretable segmentation (2022). https://doi.org/10.48550/ARXIV.2204.11618
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Tolstikhin, I.O., et al.: MLP-mixer: an all-MLP architecture for vision. In: Advances in Neural Information Processing Systems, vol. 34, pp. 24261–24272 (2021). https://doi.org/10.48550/ARXIV.2105.01601
Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1571–1580 (2020). https://doi.org/10.1109/CVPRW50498.2020.00203
Wei, J., Wang, S., Huang, Q.: F\(^3\)Net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019). https://doi.org/10.48550/ARXIV.1911.11445
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 12077–12090 (2021). https://doi.org/10.48550/ARXIV.2105.15203
Yan, H., Zhang, C., Wu, M.: Lawin transformer: improving semantic segmentation transformer with multi-scale representations via large window attention. arXiv preprint arXiv:2201.01615 (2022)
Yap, M.H., Kendrick, C., Reeves, N.D., Goyal, M., Pappachan, J.M., Cassidy, B.: Development of diabetic foot ulcer datasets: an overview. Diab. Foot Ulcers Grand Challenge 1–18 (2021)
Yap, M.H., et al.: Diabetic foot ulcers grand challenge 2022 (2021). https://doi.org/10.5281/zenodo.6389665
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Acknowledgements
This research is partially supported by the Ministry of Science and Technology (MOST) of Taiwan. We thank the National Center for High-performance Computing (NCHC) for computational and storage resources. We would also like to thank Professor Tzu-Chen Dorothy Yen and Professor Chang-Fu Kuo of Chang-Gang Memorial Hospital for their advice.
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Liao, TY., Yang, CH., Lo, YW., Lai, KY., Shen, PH., Lin, YL. (2023). HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEG for Diabetic Foot Ulcer Image 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_2
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