DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images
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 coher...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178399 |
| _version_ | 1826117179917991936 |
|---|---|
| author | 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 |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering 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 |
| author_sort | Qian, Bo |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:23:25Z |
| format | Journal Article |
| id | ntu-10356/178399 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:23:25Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1783992024-06-18T04:57:57Z 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 School of Mechanical and Aerospace Engineering Engineering Diabetic retinopathy Medical image computing 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. Published version This study was supported by the National Key Research and Development Program of China (2022YFC2502800, 2022YFA1004804, and 2022YFC2407000), the Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002) and the Chinese Academy of Engineering (2022-XY-08), National Natural Science Foundation of China (823881007, 62272298, 82270907, 82022012, and 62077037), Innovative research team of high-level local universities in Shanghai (SHSMUZDCX20212700), the Interdisciplinary Program of Shanghai Jiao Tong University (YG2023LC11, YG2022ZD007, and YG2022QN089), and the College-level Project Fund of Shanghai Sixth People’s Hospital (ynlc201909). 2024-06-18T04:57:57Z 2024-06-18T04:57:57Z 2024 Journal Article 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., ...Sheng, B. (2024). DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns, 5(3), 100929-. https://dx.doi.org/10.1016/j.patter.2024.100929 2666-3899 https://hdl.handle.net/10356/178399 10.1016/j.patter.2024.100929 38487802 2-s2.0-85186505367 3 5 100929 en Patterns © 2024 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering Diabetic retinopathy Medical image computing 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 DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title | DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title_full | DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title_fullStr | DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title_full_unstemmed | DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title_short | DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images |
| title_sort | drac 2022 a public benchmark for diabetic retinopathy analysis on ultra wide optical coherence tomography angiography images |
| topic | Engineering Diabetic retinopathy Medical image computing |
| url | https://hdl.handle.net/10356/178399 |
| work_keys_str_mv | AT qianbo drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT chenhao drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT wangxiangning drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT guanzhouyu drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT litingyao drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT jinyixiao drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT wuyilan drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT wenyang drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT chehaoxuan drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT kwongitaek drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT kimjaeyoung drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT choisungjin drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT shinseoyoung drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT krausefelix drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT unterdechlermarkus drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT houjunlin drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT fengrui drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT liyihao drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT elhabibdahomostafa drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT yangdawei drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT wuqiang drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT zhangping drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT yangxiaokang drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT caiyiyu drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT tangavinsiewwei drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT cheungcaroly drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT jiaweiping drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT lihuating drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT thamyihchung drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT wongtienyin drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages AT shengbin drac2022apublicbenchmarkfordiabeticretinopathyanalysisonultrawideopticalcoherencetomographyangiographyimages |