DRAC 2022 : A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images
© 2024 The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Patterns (New York, N.Y.) - 5(2024), 3 vom: 08. März, Seite 100929 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Qian, Bo [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 16.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.patter.2024.100929 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369774876 |
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| 520 | |a © 2024 The Author(s). | ||
| 520 | |a 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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| 700 | 1 | |a Guan, Zhouyu |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tingyao |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Yixiao |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yilan |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Che, Haoxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Kwon, Gitaek |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jaeyoung |e verfasserin |4 aut | |
| 700 | 1 | |a Choi, Sungjin |e verfasserin |4 aut | |
| 700 | 1 | |a Shin, Seoyoung |e verfasserin |4 aut | |
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| 700 | 1 | |a Unterdechler, Markus |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Junlin |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yihao |e verfasserin |4 aut | |
| 700 | 1 | |a El Habib Daho, Mostafa |e verfasserin |4 aut | |
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| 700 | 1 | |a Wu, Qiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaokang |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Yiyu |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Gavin Siew Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Carol Y |e verfasserin |4 aut | |
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