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DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.
Qian, Bo; Chen, Hao; Wang, Xiangning; Guan, Zhouyu; Li, Tingyao; Jin, Yixiao; Wu, Yilan; Wen, Yang; Che, Haoxuan; Kwon, Gitaek; Kim, Jaeyoung; Choi, Sungjin; Shin, Seoyoung; Krause, Felix; Unterdechler, Markus; Hou, Junlin; Feng, Rui; Li, Yihao; El Habib Daho, Mostafa; Yang, Dawei; Wu, Qiang; Zhang, Ping; Yang, Xiaokang; Cai, Yiyu; Tan, Gavin Siew Wei; Cheung, Carol Y; Jia, Weiping; Li, Huating; Tham, Yih Chung; Wong, Tien Yin; Sheng, Bin.
Afiliação
  • Qian B; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Chen H; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Wang X; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
  • Guan Z; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
  • Li T; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Jin Y; Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
  • Wu Y; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Wen Y; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Che H; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Kwon G; Tsinghua Medicine, Tsinghua University, Beijing 100084, China.
  • Kim J; Tsinghua Medicine, Tsinghua University, Beijing 100084, China.
  • Choi S; School of Electronic and Information Engineering, Shenzhen University, Shenzhen 518060, China.
  • Shin S; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
  • Krause F; VUNO, Inc., Seoul 06536, Korea.
  • Unterdechler M; VUNO, Inc., Seoul 06536, Korea.
  • Hou J; AI/DX Convergence Business Group, KT, Seongnam 13606, Korea.
  • Feng R; AI/DX Convergence Business Group, KT, Seongnam 13606, Korea.
  • Li Y; Johannes Kepler University Linz, Linz 4040, Austria.
  • El Habib Daho M; Johannes Kepler University Linz, Linz 4040, Austria.
  • Yang D; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
  • Wu Q; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
  • Zhang P; Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
  • Yang X; LaTIM UMR 1101, INSERM, 29609 Brest, France.
  • Cai Y; University of Western Brittany, 29238 Brest, France.
  • Tan GSW; LaTIM UMR 1101, INSERM, 29609 Brest, France.
  • Cheung CY; University of Western Brittany, 29238 Brest, France.
  • Jia W; Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.
  • Li H; Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
  • Tham YC; Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
  • Wong TY; Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Sheng B; Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA.
Patterns (N Y) ; 5(3): 100929, 2024 Mar 08.
Article em En | MEDLINE | ID: mdl-38487802
ABSTRACT
We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
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PubMed Links
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article