Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation

By Jayanthi Sivaswamy, S. R. Krishnadas, G. Dutt, Joshi, Madhulika Jain, Ujjwal Syed, Tabish A. IEEE International Symposium on Biomedical Imaging, 2014

567 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/b7ff61c0807d50266fdd3a9770a7d66399b5f087

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In Computer Vision and Pattern Recognition

In Ophthalmology

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Citations in the corpus

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.”
Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network (2017, methodology): “DRIONS-DB contains 110 full eye fundus images with optic disc segmentation; RIM-ONE v.3 159 images cropped by optic disc area, such that its diameter occupies about a ﬁfth part of an image side length, with optic disc and cup segmentation; DRISHTI-GS 50 full eye fundus images with optic disc and cup”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “Additionally, Drishti-GS ( Sivaswamy et al., 2014 ) dataset was used for data augmentation to account for the case of absence of lesions.”