GAMMA challenge: Glaucoma grAding from Multi-Modality imAges

Wu, Junde2,3; Fang, Huihui2,3; Li, Fei1; Fu, Huazhu4; Lin, Fengbin1; Lie, Jiongcheng5; Huang, Yue5; Yu, Qinji6; Song, Sifan7; Xu, Xinxing4; Xu, Yanyu4; Wang, Wensai8; Wang, Lingxiao8; Lu, Shuai9; Li, Huiqi9,18; Huang, Shihua10; Lu, Zhichao11; Ou, Chubin12; Wei, Xifei12; Liu, Bingyuan13; Kobbi, Riadh14; Tang, Xiaoying15; Lin, Li15,16; Zhou, Qiang17; Hu, Qiang17; Bogunovic, Hrvoje19; Orlando, Jose Ignacio20; Zhang, Xiulan1; Xu, Yanwu1,2,3

2023-12-01

10.1016/j.media.2023.102938

MEDICAL IMAGE ANALYSIS   影响因子和分区

1361-8415

1361-8423

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

Glaucoma Colour fundus photography Multi-modality Optical coherence tomography GAMMA challenge

SCI ; EI

英语

其他

High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University, China[303020104] ; null[A20H4b0141]

Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001091805800001

ELSEVIER

20234114860714

Color ; Cost effectiveness ; Diagnosis ; Eye protection ; Ophthalmology ; Optical tomography

Medicine and Pharmacology:461.6 ; Light/Optics:741.1 ; Optical Devices and Systems:741.3 ; Industrial Economics:911.2 ; Accidents and Accident Prevention:914.1

COMPUTER SCIENCE

Web of Science

1.Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, State Key Lab Ophthalmol, Guangdong Prov Key Lab Ophthalmol & Visual Sci, Guangzhou, Peoples R China
2.South China Univ Technol, Guangzhou, Peoples R China
3.Pazhou Lab, Guangzhou, Peoples R China
4.ASTAR, Inst High Performance Comp IHPC, Singapore, Singapore
5.Xiamen Univ, Sch Informat, Xiamen, Peoples R China
6.Shanghai Jiao Tong Univ, Shanghai, Peoples R China
7.Xian Jiaotong Liverpool Univ, Suzhou, Peoples R China
8.Chinese Acad Med Sci & Peking Union Med Coll, Inst Biomed Engn, Tianjin, Peoples R China
9.Beijing Inst Technol, Sch Med Technol, Beijing, Peoples R China
10.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
11.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China
12.Weizhi Med Technol Co, Suzhou, Peoples R China
13.Ecole Technol Super, Montreal, PQ, Canada
14.DIAGNOS Inc, Quebec City, PQ, Canada
15.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
16.Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China
17.Suixin Shanghai Technol Co Ltd, Shanghai, Peoples R China
18.Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China
19.Med Univ Vienna, Dept Ophthalmol, Christian Doppler Lab Artificial Intelligence Ret, Vienna, Austria
20.PLADEMA Inst, Yatiris Grp, PLADEMA Inst, Yatiris Grp, Tandil, Argentina

Wu, Junde,Fang, Huihui,Li, Fei,et al. GAMMA challenge: Glaucoma grAding from Multi-Modality imAges[J]. MEDICAL IMAGE ANALYSIS,2023,90.

Wu, Junde.,Fang, Huihui.,Li, Fei.,Fu, Huazhu.,Lin, Fengbin.,...&Xu, Yanwu.(2023).GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.MEDICAL IMAGE ANALYSIS,90.

Wu, Junde,et al."GAMMA challenge: Glaucoma grAding from Multi-Modality imAges".MEDICAL IMAGE ANALYSIS 90(2023).