ORIGA-light: An online retinal fundus image database for glaucoma analysis and research
By Zhuo Zhang, F. Yin, Jiang Liu, Wing Kee Wong, N. Tan, B. Lee, Jun Cheng, Tien Yin Wong. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
556 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/47ec115fb91adcd00716467fa038b0507ad6ac41
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Ophthalmology
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- RIM-ONE: An open retinal image database for optic nerve evaluation (2011, methodology): “One database that is explicitly related to glaucoma analysis and research is ORIGA-light [10], containing 650 retinal fundus images, annotated with grading information for diagnosis of glaucoma and with their respective cup and disc manual segmentations, but its web interface does not seem to be pub”
- Glaucoma detection based on deep convolutional neural network (2015, methodology): “The AUC values of our method on ORIGA and SCES are 0.831 and 0.887, respectively.”
- CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation (2020, background): “Fudus OD & OC images: 2068 images from five datasets (Refuge [19], Drishti-GS [23], ORIGA [26], RIGA [1], RIM-ONE [8]) are merged together.”
- REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, unknown): “rding the evaluated proposals and their dierences in training settings, particularly those related with data availability. Despite the fact that ORIGA is claimed to be publicly available since 2010 (Zhang et al., 2010), by the time of this publication it was not possible to download the images. Thes”
- CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “In particular, it achieves an overlapping error of 0.058 in the ORIGA dataset, a relative reduction of 15.9% from 0.069 by the latest Faster RCNN or DeepDisc methods.”
- Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018, methodology): “We employ the ORIGA dataset [40] containing 650 fundus images with 168 glaucomatous eyes and 482 normal eyes.”