IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Journal: Medical image analysis
Published Date: Oct 3, 2019

Abstract

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.

Authors

  • Prasanna Porwal
    Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA. Electronic address: porwalprasanna@sggs.ac.in.
  • Samiksha Pachade
    Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA.
  • Manesh Kokare
    Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra 431606, India.
  • Girish Deshmukh
    Eye Clinic, Sushrusha Hospital, Nanded, Maharashtra, India.
  • Jaemin Son
    VUNO Inc., Seoul, Korea.
  • Woong Bae
    Soombit.ai, Seoul, South Korea.
  • Lihong Liu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Jianzong Wang
    Ping An Technology (Shenzhen) Co.,Ltd, China.
  • Xinhui Liu
    Ping An Technology (Shenzhen) Co.,Ltd, China.
  • Liangxin Gao
    Ping An Technology (Shenzhen) Co.,Ltd, China.
  • TianBo Wu
    Ping An Technology (Shenzhen) Co.,Ltd, China.
  • Jing Xiao
    Xiyuan Hospital, China Academy of Chinese Medical Sciences(CACMS), Beijing, China.
  • Fengyan Wang
    iFLYTEK Research, Hefei, China.
  • Baocai Yin
    iFLYTEK Research, Hefei, China.
  • Yunzhi Wang
    School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
  • Gopichandh Danala
  • Linsheng He
    School of Electrical and Computer Engineering, University of Oklahoma, USA.
  • Yoon Ho Choi
    Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Yeong Chan Lee
    Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Sang-Hyuk Jung
    Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Zhongyu Li
    Institute of Pathogenic Biology, School of Nursing, Hengyang Medical College, Hunan Provincial Key Laboratory for Special Pathogens Prevention and Control, University of South China, Hengyang 421001, China.
  • Xiaodan Sui
    School of Information Science and Engineering, Shandong Normal University, China.
  • Junyan Wu
    Cleerly Inc., New York, United States.
  • Xiaolong Li
    Auckland Tongji Medical & Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China.
  • Ting Zhou
    Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
  • Janos Toth
    University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary.
  • Agnes Baran
    University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary.
  • Avinash Kori
    Individual Researcher, India.
  • Sai Saketh Chennamsetty
    Bangalore, India.
  • Mohammed Safwan
    Gurgaon, India.
  • Varghese Alex
    Chennai, India.
  • Xingzheng Lyu
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore.
  • Li Cheng
  • Qinhao Chu
    School of Computing, National University of Singapore, Singapore.
  • Pengcheng Li
    Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China. Electronic address: pcli@qdio.ac.cn.
  • Xin Ji
    Beijing Shanggong Medical Technology Co., Ltd., China.
  • Sanyuan Zhang
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Yaxin Shen
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China.
  • Ling Dai
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China.
  • Oindrila Saha
    Indian Institute of Technology Kharagpur, India.
  • Rachana Sathish
    Indian Institute of Technology Kharagpur, India.
  • Tânia Melo
    INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.
  • Teresa Araújo
    Faculdade de Engenharia da Universidade do Porto (FEUP), R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
  • Balázs Harangi
    Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
  • Bin Sheng
    MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Ruogu Fang
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL.
  • Debdoot Sheet
    Department of Electrical Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.
  • Andras Hajdu
    University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary.
  • Yuanjie Zheng
  • Ana Maria Mendonca
  • Shaoting Zhang
  • Aurélio Campilho
    Faculdade de Engenharia da Universidade do Porto (FEUP), R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
  • Bin Zheng
    School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Luca Giancardo
    Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.
  • Gwenolé Quellec
    Inserm, UMR 1101, 22 avenue Camille-Desmoulins, Brest F-29200, France. Electronic address: gwenole.quellec@inserm.fr.
  • Fabrice Meriaudeau
    LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France. fabrice.meriaudeau@utp.edu.my.

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