Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

Ophthalmology

Volumn 125, Issue 4, 2018, Pages 549-558

  Schlegl, Thomas ^a   Waldstein, Sebastian M ^a   Bogunovic, Hrvoje ^a   Endstraßer, Franz ^a   Sadeghipour, Amir ^a   Philip, Ana Maria ^a   Podkowinski, Dominika ^a   Gerendas, Bianca S ^a   Langs, Georg ^a   Schmidt Erfurth, Ursula ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; DEEP LEARNING; DIABETIC MACULAR EDEMA; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; HUMAN; LEARNING ALGORITHM; MACHINE LEARNING; MAJOR CLINICAL STUDY; OPTICAL COHERENCE TOMOGRAPHY; PRIORITY JOURNAL; RECALL; RECEIVER OPERATING CHARACTERISTIC; RETINA IMAGE; RETINA VEIN OCCLUSION; SUBRETINAL FLUID; WET MACULAR DEGENERATION; AGED; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; DIABETIC RETINOPATHY; DIAGNOSTIC IMAGING; FEMALE; MACULAR EDEMA; MALE; MIDDLE AGED; PROCEDURES; REPRODUCIBILITY; VALIDATION STUDY; VISUAL ACUITY;

AGED; DEEP LEARNING; DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MACULAR EDEMA; MALE; MIDDLE AGED; REPRODUCIBILITY OF RESULTS; RETINAL VEIN OCCLUSION; ROC CURVE; SUBRETINAL FLUID; TOMOGRAPHY, OPTICAL COHERENCE; VISUAL ACUITY; WET MACULAR DEGENERATION;

EID: 85044656432     PISSN: 01616420     EISSN: 15494713     Source Type: Journal    
DOI: 10.1016/j.ophtha.2017.10.031     Document Type: Article

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