A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes

Biomedical Optics Express

Volumn 4, Issue 12, 2013, Pages 2712-2728

  Antony, Bhavna J ^a   Abràmoff, Michael D ^a,b,c,e   Harper, Matthew M ^b,c   Jeong, Woojin ^b,d   Sohn, Elliott H ^b,e   Kwon, Young H ^b   Kardon, Randy ^b,c   Garvin, Mona K ^a,c  

^a UNIVERSITY OF IOWA   (United States)

^b UNIVERSITY OF IOWA   (United States)

^c VETERANS AFFAIRS MEDICAL CENTER   (United States)

^d Dong A University   (South Korea)

^e Stephen A Wynn Institute for Vision Research   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED SEGMENTATION METHOD; GLOBAL OPTIMALITY; GRAPH THEORETIC APPROACH; GRAPH-THEORETIC METHODS; MACHINE-LEARNING; POSITION ERRORS; SEGMENTATION ACCURACY; TIME-CONSUMING TASKS;

AUTOMATION; COST FUNCTIONS; GRAPH THEORY; LEARNING SYSTEMS; MAMMALS; OPHTHALMOLOGY;

OPTICAL TOMOGRAPHY;

ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER AIDED DESIGN; DOG; GRAPH THEORETIC METHOD; HUMAN; MACHINE LEARNING; MEASUREMENT ACCURACY; MOUSE; NONHUMAN; PIGMENT EPITHELIUM; RETINA; RETINA BIPOLAR GANGLION CELL; RETINA EXTERNAL LIMITING MEMBRANE; RETINA GANGLION CELL LAYER; RETINA INNER PLEXIFORM LAYER; RETINA OUTER MOLECULAR LAYER; RETINAL NERVE FIBER LAYER; RETINAL SEGMENTATION; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY; THEORETICAL MODEL; VISUAL SYSTEM PARAMETERS;

EID: 84888807879     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.4.002712     Document Type: Article

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