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Diabetic foot ulcers segmentation challenge report: Benchmark and analysis.
Yap, Moi Hoon; Cassidy, Bill; Byra, Michal; Liao, Ting-Yu; Yi, Huahui; Galdran, Adrian; Chen, Yung-Han; Brüngel, Raphael; Koitka, Sven; Friedrich, Christoph M; Lo, Yu-Wen; Yang, Ching-Hui; Li, Kang; Lao, Qicheng; Ballester, Miguel A González; Carneiro, Gustavo; Ju, Yi-Jen; Huang, Juinn-Dar; Pappachan, Joseph M; Reeves, Neil D; Chandrabalan, Vishnu; Dancey, Darren; Kendrick, Connah.
Afiliación
  • Yap MH; Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom; Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, United Kingdom. Electronic address: m.yap@mmu.ac.uk.
  • Cassidy B; Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom.
  • Byra M; Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland; RIKEN Center for Brain Science, Wako, Japan.
  • Liao TY; Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan.
  • Yi H; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Galdran A; BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain; AIML, University of Adelaide, Australia.
  • Chen YH; Institute of Electronics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 300, Taiwan.
  • Brüngel R; Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), Emil-Figge-Str. 42, 44227 Dortmund, Germany; Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Zweigertstr. 37, 45130 Essen, Germany; Institute for Artificia
  • Koitka S; Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstr. 2, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147 Essen, Germany.
  • Friedrich CM; Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), Emil-Figge-Str. 42, 44227 Dortmund, Germany; Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Zweigertstr. 37, 45130 Essen, Germany.
  • Lo YW; Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan.
  • Yang CH; Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan.
  • Li K; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Lao Q; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Ballester MAG; BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.
  • Carneiro G; University of Surrey, Guildford, United Kingdom.
  • Ju YJ; Institute of Electronics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 300, Taiwan.
  • Huang JD; Institute of Electronics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 300, Taiwan.
  • Pappachan JM; Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, United Kingdom; Department of Life Sciences, Manchester Metropolitan University, Manchester, M1 5GD, United Kingdom.
  • Reeves ND; Department of Life Sciences, Manchester Metropolitan University, Manchester, M1 5GD, United Kingdom.
  • Chandrabalan V; Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, United Kingdom.
  • Dancey D; Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom.
  • Kendrick C; Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom.
Med Image Anal ; 94: 103153, 2024 May.
Article en En | MEDLINE | ID: mdl-38569380
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pie Diabético / Diabetes Mellitus Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pie Diabético / Diabetes Mellitus Límite: Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos