REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Autor: Orlando, Jos�� Ignacio, Fu, Huazhu, Breda, Jo��o Barbossa, van Keer, Karel, Bathula, Deepti R., Diaz-Pinto, Andr��s, Fang, Ruogu, Heng, Pheng-Ann, Kim, Jeyoung, Lee, JoonHo, Lee, Joonseok, Li, Xiaoxiao, Liu, Peng, Lu, Shuai, Murugesan, Balamurali, Naranjo, Valery, Phaye, Sai Samarth R., Shankaranarayana, Sharath M., Sikka, Apoorva, Son, Jaemin, Hengel, Anton van den, Wang, Shujun, Wu, Junyan, Wu, Zifeng, Xu, Guanghui, Xu, Yongli, Yin, Pengshuai, Li, Fei, Zhang, Xiulan, Xu, Yanwu, Bogunovi��, Hrvoje
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
genetic structures
Computer science
Image classification
Fundus Oculi
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Glaucoma
Datasets as Topic
Health Informatics
Fundus (eye)
Diagnostic Techniques
Ophthalmological
Machine learning
computer.software_genre
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Deep Learning
TEORIA DE LA SEÑAL Y COMUNICACIONES
medicine
Photography
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Ground truth
Image segmentation
Modality (human–computer interaction)
Radiological and Ultrasound Technology
Contextual image classification
medicine.diagnostic_test
business.industry
Fundus photography
Deep learning
medicine.disease
Computer Graphics and Computer-Aided Design
eye diseases
Computer Vision and Pattern Recognition
Artificial intelligence
sense organs
business
computer
030217 neurology & neurosurgery
Zdroj: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
ISSN: 1361-8423
Popis: Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (\url{https://refuge.grand-challenge.org}), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.
Comment: Accepted for publication in Medical Image Analysis
Databáze: OpenAIRE
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