Instytut Podstawowych Problemów Techniki
Polskiej Akademii Nauk

Partnerzy

Chen Yung-Han


Ostatnie publikacje
1.  Yap M., Bill C., Byra M., Ting-yu L., Huahu Y., Galdran A., Yung-Han C., Raphael B., Sven K., Friedrich C., Yu-wen L., Ching-hui Y., Kang L., Qicheng L., Ballester M., Carneiro G., Yi-Jen J., Juinn-Dar H., Pappachan J., Reeves N., Vishnu C., Darren D., Diabetic foot ulcers segmentation challenge report: Benchmark and analysis, Medical Image Analysis, ISSN: 1361-8415, DOI: 10.1016/j.media.2024.103153, Vol.94, No.103153, pp.1-14, 2024

Streszczenie:
Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.

Słowa kluczowe:
Deep learning, Diabetic foot ulcers, Segmentation, Convolutional neural networks

Afiliacje autorów:
Yap M. - inna afiliacja
Bill C. - inna afiliacja
Byra M. - IPPT PAN
Ting-yu L. - inna afiliacja
Huahu Y. - inna afiliacja
Galdran A. - inna afiliacja
Yung-Han C. - inna afiliacja
Raphael B. - inna afiliacja
Sven K. - inna afiliacja
Friedrich C. - inna afiliacja
Yu-wen L. - inna afiliacja
Ching-hui Y. - inna afiliacja
Kang L. - inna afiliacja
Qicheng L. - inna afiliacja
Ballester M. - inna afiliacja
Carneiro G. - inna afiliacja
Yi-Jen J. - inna afiliacja
Juinn-Dar H. - inna afiliacja
Pappachan J. - inna afiliacja
Reeves N. - inna afiliacja
Vishnu C. - inna afiliacja
Darren D. - inna afiliacja
200p.

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