Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

By Meindert Niemeijer, B. van Ginneken, Michael J. Cree, Atsushi Mizutani, Gwenolé Quellec, Clara I. Sánchez, Bob Zhang, Roberto Hornero, Mathieu Lamard, C. Muramatsu and 12 more. IEEE Transactions on Medical Imaging, 2010

572 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/378dddb8472d1dd8e8bf18fc670916cdda4d4c1e

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Citations in the corpus

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, unknown): “• AUC: Area Under the (ROC) Curve.”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “…medical image segmentation and annotation include algorithms for artery centerline extraction [61], [62], vessel segmentation and stenosis grading [63], liver segmentation [64], [65], detection of microaneurysms in digital color fundus photographs [66], and extraction of airways from CT scans [67].”
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE (2014, methodology): “, 2012), ROC (Niemeijer et al., 2010) and e-ophtha (Decencière et al.”
Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases (2018, unknown): “In this study, the sensitivity values against the average number of false positives per image (FPI) was used to measure the MA detection performance [4], and sensitivity values for FPI rates of 1/8, 1/4, 1/2, 1, 2, 4, and 8 were used.”
Computer Methods and Programs in Biomedicine (unknown): “…[137] 143 CFP CHASE [138] 28 CFP HRF [139] 45 CFP IMAGERET [140] 219 CFP MESSIDOR 1200 CFP ( http://messidor.crihan.fr ) REVIEW [141] 16 CFP ROC [142] 100 CFP VICAVR 58 CFP ( http://www.varpa.es ) VAMPIRE 8 FA ( http://vampire.computing . dundee.ac.uk) CASDQEF [143] Coronary 48 CTA ROTTERDAM…”
Automated Detection of Diabetic Retinopathy using Deep Learning (2018, background): “Past studies using various high bias, low variance digital image processing techniques have performed well at identifying one speciﬁc feature used in the detection of subtle disease such as the use of top-hat algorithm for microaneurysm detection 17 23 16 .”
Identification and classification of microaneurysms for early detection of diabetic retinopathy (2013, background): “The results of first international competition were reported in [15].”
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, background): “…been organized to solve similar issues in the ﬁelds of liver segmentation on CT (Heimann et al., 2009), coronary image analysis (Schaap et al., 2009), brain segmentation on MR (Shattuck et al., 2009), retinal image analysis (Niemeijer et al., 2010) and lung registration on CT (Murphy et al., 2011).”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “…groups to develop and share retinal image datasets, namely Messidor ( Decencière et al., 2014 ), Kaggle ( Cuadros and Bresnick, 2009 ), ROC ( Niemeijer et al., 2010 ), E-Ophtha ( Decencière et al., 2013 ), DiaretDB ( Kauppi et al., 2012 ), DRIVE ( van Ginneken et al., 2004 ), STARE (…”
An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading (2012, methodology): “This level is approximately 1 FP per image [13] for the ROC database, on which level our ensemble achieved the best score in the competition.”
Retinal Imaging and Image Analysis (2010, unknown): “Fundus imaging examples are represented by the STARE [72], DRIVE [73], REVIEW [99] and MESSIDOR databases [100], with large numbers of annotated retinal fundus images, with expert annotations for vessel segmentation, vessel width measurements, and diabetic retinopathy detection, as well as competiti”