{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:17:31Z","timestamp":1772734651928,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UWM Research Foundation Catalyst Grant"},{"name":"UWM Discovery and Innovation Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.<\/jats:p>","DOI":"10.3390\/info15030140","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T03:31:23Z","timestamp":1709263883000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["FUSeg: The Foot Ulcer Segmentation Challenge"],"prefix":"10.3390","volume":"15","author":[{"given":"Chuanbo","family":"Wang","sequence":"first","affiliation":[{"name":"Big Data Analytics and Visualization Laboratory, Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-1442","authenticated-orcid":false,"given":"Amirreza","family":"Mahbod","sequence":"additional","affiliation":[{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, 3500 Krems an der Donau, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6335-076X","authenticated-orcid":false,"given":"Isabella","family":"Ellinger","sequence":"additional","affiliation":[{"name":"Institute for Pathophysiology and Allergy Research, Medical University of Vienna, 1090 Vienna, Austria"}]},{"given":"Adrian","family":"Galdran","sequence":"additional","affiliation":[{"name":"Department of Computing and Informatics, Bournemouth University, Bournemouth BH12 5BB, UK"}]},{"given":"Sandeep","family":"Gopalakrishnan","sequence":"additional","affiliation":[{"name":"Wound Healing and Tissue Repair Laboratory, School of Nursing, College of Health Professions and Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA"}]},{"given":"Jeffrey","family":"Niezgoda","sequence":"additional","affiliation":[{"name":"Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center, Milwaukee, WI 53211, USA"}]},{"given":"Zeyun","family":"Yu","sequence":"additional","affiliation":[{"name":"Big Data Analytics and Visualization Laboratory, Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1089\/wound.2015.0635","article-title":"Challenges in the Treatment of Chronic Wounds","volume":"4","author":"Frykberg","year":"2015","journal-title":"Adv. Wound Care"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1089\/wound.2019.0946","article-title":"Human Wounds and Its Burden: An Updated Compendium of Estimates","volume":"8","author":"Sen","year":"2019","journal-title":"Adv. Wound Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.burns.2008.03.009","article-title":"A review of gene and stem cell therapy in cutaneous wound healing","volume":"35","author":"Branski","year":"2009","journal-title":"Burns"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jval.2017.07.007","article-title":"An Economic Evaluation of the Impact, Cost, and Medicare Policy Implications of Chronic non-healing Wounds","volume":"21","author":"Nussbaum","year":"2018","journal-title":"Value Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yap, M.H., Cassidy, B., Pappachan, J.M., O\u2019Shea, C., Gillespie, D., and Reeves, N.D. (2021, January 27\u201330). Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers. Proceedings of the EMBS International Conference on Biomedical and Health Informatics, Athens, Greece.","DOI":"10.1109\/BHI50953.2021.9508563"},{"key":"ref_6","unstructured":"Thomas, S. (2024, January 22). Medetec Wound Database. Available online: http:\/\/www.medetec.co.uk\/files\/medetec-image-databases.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101844","DOI":"10.1016\/j.compmedimag.2020.101844","article-title":"Chronic wounds multimodal image database","volume":"88","author":"Czajkowska","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association Between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Schaefer, G., Ecker, R., and Ellinger, I. (2022, January 21\u201325). Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks. Proceedings of the 26th International Conference on Pattern Recognition, Montreal, QC, Canada.","DOI":"10.1109\/ICPR56361.2022.9956253"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). LinkNet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the IEEE Visual Communications and Image Processing, St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_12","unstructured":"Chaudhuri, K., and Salakhutdinov, R. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA. Proceedings of Machine Learning Research PMLR, PMLR."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Schaefer, G., Ecker, R., and Ellinger, I. (2021, January 10\u201315). Pollen grain microscopic image classification using an ensemble of fine-tuned deep convolutional neural networks. Proceedings of the International Conference on Pattern Recognition, Virtual.","DOI":"10.1007\/978-3-030-68763-2_26"},{"key":"ref_14","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s12204-021-2264-x","article-title":"Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images","volume":"26","author":"Zhang","year":"2021","journal-title":"J. Shanghai Jiaotong Univ. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5068","DOI":"10.1038\/s41598-020-61808-3","article-title":"Test-time augmentation for deep learning-based cell segmentation on microscopy images","volume":"10","author":"Moshkov","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tsiknakis, N., Savvidaki, E., Manikis, G.C., Gotsiou, P., Remoundou, I., Marias, K., Alissandrakis, E., and Vidakis, N. (2022). Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset. Plants, 11.","DOI":"10.3390\/plants11070919"},{"key":"ref_18","unstructured":"Nguyen, H.V., Huang, S.X., and Xue, Y. (2022, January 22). TAAL: Test-Time Augmentation for Active Learning in Medical Image Segmentation. Proceedings of the Data Augmentation, Labelling, and Imperfections, Singapore."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21897","DOI":"10.1038\/s41598-020-78799-w","article-title":"Fully automatic wound segmentation with deep convolutional neural networks","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","unstructured":"Huang, C.H., Wu, H.Y., and Lin, Y.L. (2021). HarDNet-MSEG: A simple encoder\u2013decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 15\u201320). Cascaded Partial Decoder for Fast and Accurate Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang, A. (2018, January 8\u201314). Receptive Field Block Net for Accurate and Fast Object Detection. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Galdran, A., Carneiro, G., and Ballester, M.A.G. (2021, January 10\u201315). Double encoder\u2013decoder Networks for Gastrointestinal Polyp Segmentation. Proceedings of the International Conference on Pattern Recognition, Virtual.","DOI":"10.1007\/978-3-030-68763-2_22"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1093\/schbul\/17.3.483","article-title":"Measurement and Reliability: Statistical Thinking Considerations","volume":"17","author":"Bartko","year":"1991","journal-title":"Schizophr. Bull."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1186\/s13244-023-01487-6","article-title":"An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images","volume":"14","author":"Boehringer","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/42.363096","article-title":"Morphometric analysis of white matter lesions in MR images: Method and validation","volume":"13","author":"Zijdenbos","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103000","DOI":"10.1016\/j.media.2023.103000","article-title":"Advances in medical image analysis with vision Transformers: A comprehensive review","volume":"91","author":"Azad","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","article-title":"Transformers in medical image analysis","volume":"3","author":"He","year":"2023","journal-title":"Intell. Med."},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_35","unstructured":"Meila, M., and Zhang, T. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the 38th International Conference on Machine Learning, Virtual. Proceedings of Machine Learning Research PMLR, PMLR."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H. (2021, January 20\u201325). Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.csbj.2023.12.042","article-title":"Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization","volume":"23","author":"Mahbod","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_38","unstructured":"Wen, Y., Tran, D., and Ba, J. (2020). BatchEnsemble: An alternative approach to efficient ensemble and lifelong learning. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/3\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:07:42Z","timestamp":1760105262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/3\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["info15030140"],"URL":"https:\/\/doi.org\/10.3390\/info15030140","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,1]]}}}