RIM-ONE: An open retinal image database for optic nerve evaluation

By F. Fumero, S. Alayón, José L. Sánchez, J. Sigut, M. Gonzalez-Hernandez. Computer-Based Medical Systems, 2011

604 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/de0ba8f29739620b9d09c87fecfe6507e73d5c7d

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In Computer Vision and Pattern Recognition

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Retinal Image Understanding (2016, methodology): “Optic Disc Segmentation: We experiment on the DRIONS-DB [2] and RIM-ONE (r3) [6] datasets (110 and 159 images, respectively).”
CNNs for automatic glaucoma assessment using fundus images: an extensive validation (2019, methodology): “…from the ACRIMA database, other four public databases were used in this work: HRF database [25], which contains 45 images; Drishti-GS1 database [4], which consists of 101 images; RIM-ONE database [26], which is composed of 455 images; and sjchoi86-HRF [27] database which is composed of 401 images.”
Generative Adversarial Network in Medical Imaging: A Review (2018, background): “D49 Fumero et al. (2011) RIM-ONE Optic nerve head segmentation Eye Fundus Imaging D50 Hobson et al.”
Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network (2017, background): “3 [4], DRISHTI-GS [15, 16], which contain groundtruth segmentation for optic disc (and some for optic cup as well).”
Applications of Deep Learning in Fundus Images: A Review (2021, neutral): “The RIM-ONE-r3 dataset (Fumero et al., 2011) consists of 169 ONH images, of which 118 are normal, 11 have ocular hypertension (OHT) and 40 are glaucomatous.”
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, positive): “Each network was independently fine-tuned from the weights pre-trained from ImageNet (Russakovsky et al., 2015) to identify glaucomatous images, using DRISHTI-GS1, HRF, ORIGA, RIM-ONE and the training set of the REFUGE databases.”
Federated learning for medical image analysis: A survey (2023, neutral): “(2023) [130] Fundus segmentation RIM-ONE [131] Color retinal image CNN (MobileNet) Wang et al.”
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2021, methodology): “We employ retinal fundus images from 4 different clinical centers of public datasets [52, 10, 40] for optic disc and cup segmentation.”
CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “Three datasets, ORIGA [66], Messidor [67] and RIMONE-R1 [68], are used in our experiments.”
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation (2014, background): “Structural changes in the ONH, which consists of optic disk (OD) and cup region, is one of the standard way to assess the amount of glaucomatous damage.”