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

doi:10.1016/j.patter.2024.100929

. 2024 Feb 8;5(3):100929.

doi: 10.1016/j.patter.2024.100929. eCollection 2024 Mar 8.

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

Bo Qian^{1

2}, Hao Chen^{3

4}, Xiangning Wang^{1

5}, Zhouyu Guan¹, Tingyao Li^{1

2}, Yixiao Jin⁶, Yilan Wu⁶, Yang Wen⁷, Haoxuan Che³, Gitaek Kwon⁸, Jaeyoung Kim⁸, Sungjin Choi⁹, Seoyoung Shin⁹, Felix Krause¹⁰, Markus Unterdechler¹⁰, Junlin Hou¹¹, Rui Feng^{11

12}, Yihao Li^{13

14}, Mostafa El Habib Daho^{13

14}, Dawei Yang¹⁵, Qiang Wu⁵, Ping Zhang^{16

17

18}, Xiaokang Yang², Yiyu Cai¹⁹, Gavin Siew Wei Tan²⁰, Carol Y Cheung¹⁵, Weiping Jia¹, Huating Li¹, Yih Chung Tham^{20

21

22}, Tien Yin Wong^{6

20

23}, Bin Sheng^{1

2}

Affiliations

¹ 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 Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
² MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
³ Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
⁴ Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
⁵ Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
⁶ Tsinghua Medicine, Tsinghua University, Beijing 100084, China.
⁷ School of Electronic and Information Engineering, Shenzhen University, Shenzhen 518060, China.
⁸ VUNO, Inc., Seoul 06536, Korea.
⁹ AI/DX Convergence Business Group, KT, Seongnam 13606, Korea.
¹⁰ Johannes Kepler University Linz, Linz 4040, Austria.
¹¹ School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
¹² Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
¹³ LaTIM UMR 1101, INSERM, 29609 Brest, France.
¹⁴ University of Western Brittany, 29238 Brest, France.
¹⁵ Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.
¹⁶ Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
¹⁷ Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
¹⁸ Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA.
¹⁹ School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
²⁰ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore.
²¹ Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
²² Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore.
²³ School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing 102218, China.

PMID: 38487802
PMCID: PMC10935505
DOI: 10.1016/j.patter.2024.100929

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

Bo Qian et al. Patterns (N Y). 2024.

. 2024 Feb 8;5(3):100929.

doi: 10.1016/j.patter.2024.100929. eCollection 2024 Mar 8.

Authors

Affiliations

¹ 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 Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
² MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
³ Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
⁴ Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
⁵ Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
⁶ Tsinghua Medicine, Tsinghua University, Beijing 100084, China.
⁷ School of Electronic and Information Engineering, Shenzhen University, Shenzhen 518060, China.
⁸ VUNO, Inc., Seoul 06536, Korea.
⁹ AI/DX Convergence Business Group, KT, Seongnam 13606, Korea.
¹⁰ Johannes Kepler University Linz, Linz 4040, Austria.
¹¹ School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
¹² Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
¹³ LaTIM UMR 1101, INSERM, 29609 Brest, France.
¹⁴ University of Western Brittany, 29238 Brest, France.
¹⁵ Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.
¹⁶ Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
¹⁷ Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
¹⁸ Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA.
¹⁹ School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
²⁰ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore.
²¹ Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
²² Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore.
²³ School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing 102218, China.

PMID: 38487802
PMCID: PMC10935505
DOI: 10.1016/j.patter.2024.100929

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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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
Examples of UW-OCTA images in three tasks of the challenge IRMA, intraretinal microvascular abnormality; NPA, nonperfusion area; NV, neovascularization; non-DR, non-diabetic retinopathy; NPDR, non-proliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy.

**Figure 2**
Bar charts of the final rankings for three tasks The colored bars show the ensemble and the top three scores in each task. Ensemble represents the ensemble results of the top three algorithms.

**Figure 3**
Confusion matrix for image quality assessment task and DR-grading task The top row is the image quality assessment task and the bottom row is the DR-grading task. From left to right are the ensemble and first-, second-, and third-ranked results, respectively. Ensemble represents the ensemble results of the top three algorithms.

See this image and copyright information in PMC

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References

1. Reichel E., Salz D. In: Managing Diabetic Eye Disease in Clinical Practice. Singh R.P., editor. Springer International Publishing; 2015. Diabetic retinopathy screening; pp. 25–38. - DOI
1. International Diabetes Federation (IDF); Brussels, Belgium: 2017. IDF Diabetes Atlas. p. 147.
1. Wang W., Lo A.C.Y. Diabetic Retinopathy: Pathophysiology and Treatments. Int. J. Mol. Sci. 2018;19:1816. doi: 10.3390/ijms19061816. - DOI - PMC - PubMed
1. American Diabetes Association 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2020. Diabetes Care. 2020;43:S111–S134. doi: 10.2337/dc20-S010. - DOI - PubMed
1. Guan Z., Li H., Liu R., Cai C., Liu Y., Li J., Wang X., Huang S., Wu L., Liu D., et al. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep. Med. 2023;4 doi: 10.1016/j.xcrm.2023.101213. - DOI - PMC - PubMed

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[1] Reichel E., Salz D. In: Managing Diabetic Eye Disease in Clinical Practice. Singh R.P., editor. Springer International Publishing; 2015. Diabetic retinopathy screening; pp. 25–38. - DOI

[2] Reichel E., Salz D. In: Managing Diabetic Eye Disease in Clinical Practice. Singh R.P., editor. Springer International Publishing; 2015. Diabetic retinopathy screening; pp. 25–38. - DOI

[3] International Diabetes Federation (IDF); Brussels, Belgium: 2017. IDF Diabetes Atlas. p. 147.

[4] International Diabetes Federation (IDF); Brussels, Belgium: 2017. IDF Diabetes Atlas. p. 147.

[5] Wang W., Lo A.C.Y. Diabetic Retinopathy: Pathophysiology and Treatments. Int. J. Mol. Sci. 2018;19:1816. doi: 10.3390/ijms19061816. - DOI - PMC - PubMed

[6] Wang W., Lo A.C.Y. Diabetic Retinopathy: Pathophysiology and Treatments. Int. J. Mol. Sci. 2018;19:1816. doi: 10.3390/ijms19061816. - DOI - PMC - PubMed

[7] American Diabetes Association 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2020. Diabetes Care. 2020;43:S111–S134. doi: 10.2337/dc20-S010. - DOI - PubMed

[8] American Diabetes Association 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2020. Diabetes Care. 2020;43:S111–S134. doi: 10.2337/dc20-S010. - DOI - PubMed

[9] Guan Z., Li H., Liu R., Cai C., Liu Y., Li J., Wang X., Huang S., Wu L., Liu D., et al. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep. Med. 2023;4 doi: 10.1016/j.xcrm.2023.101213. - DOI - PMC - PubMed

[10] Guan Z., Li H., Liu R., Cai C., Liu Y., Li J., Wang X., Huang S., Wu L., Liu D., et al. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep. Med. 2023;4 doi: 10.1016/j.xcrm.2023.101213. - DOI - PMC - PubMed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

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Abstract

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