Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy

Biomedical Optics Express

Volumn 8, Issue 9, 2017, Pages 4061-4076

  Xu, Yupeng ^a,b   Yan, Ke ^c   Kim, Jinman ^c   Wang, Xiuying ^c   Li, Changyang ^c   Su, Li ^a,b   Yu, Suqin ^a,b   Xu, Xun ^a,b   Feng, Dagan David ^c  

^a SHANGHAI JIAO TONG UNIVERSITY SCHOOL OF MEDICINE   (China)

^b SHANGHAI ENGINEERING CENTER FOR VISUAL SCIENCE AND PHOTOMEDICINE   (China)

^c UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Image processing; Medical and biological imaging; Optical coherence tomography; Pattern recognition, neural networks

Indexed keywords

DEEP NEURAL NETWORKS; DIAGNOSIS; IMAGE PROCESSING; MEDICAL IMAGING; OPTICAL DATA PROCESSING; OPTICAL TOMOGRAPHY; PATTERN RECOGNITION; TOMOGRAPHY; VOLUME FRACTION;

CLINICAL CHARACTERISTICS; MANUAL SEGMENTATION; MEDICAL AND BIOLOGICAL IMAGING; POSITIVE PREDICTIVE VALUES; QUANTITATIVE INFORMATION; SEGMENTATION ACCURACY; SEGMENTATION METHODS; SIMILARITY COEFFICIENTS;

DEEP LEARNING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; DUAL STAGE LEARNING FRAMEWORK; IMAGE PROCESSING; IMAGE SEGMENTATION; MACHINE LEARNING; MATHEMATICAL MODEL; OPTICAL COHERENCE TOMOGRAPHY; POLYPOIDAL CHOROIDAL VASCULOPATHY; RETINA DETACHMENT;

EID: 85028768220     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.8.004061     Document Type: Article

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