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DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

Authors :
Qian B
Chen H
Wang X
Guan Z
Li T
Jin Y
Wu Y
Wen Y
Che H
Kwon G
Kim J
Choi S
Shin S
Krause F
Unterdechler M
Hou J
Feng R
Li Y
El Habib Daho M
Yang D
Wu Q
Zhang P
Yang X
Cai Y
Tan GSW
Cheung CY
Jia W
Li H
Tham YC
Wong TY
Sheng B
Source :
Patterns (New York, N.Y.) [Patterns (N Y)] 2024 Feb 08; Vol. 5 (3), pp. 100929. Date of Electronic Publication: 2024 Feb 08 (Print Publication: 2024).
Publication Year :
2024

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.<br />Competing Interests: The authors declare no competing interests.<br /> (© 2024 The Author(s).)

Details

Language :
English
ISSN :
2666-3899
Volume :
5
Issue :
3
Database :
MEDLINE
Journal :
Patterns (New York, N.Y.)
Publication Type :
Academic Journal
Accession number :
38487802
Full Text :
https://doi.org/10.1016/j.patter.2024.100929