Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks

Neurocomputing

Volumn 237, Issue , 2017, Pages 332-341

  Sui, Xiaodan ^a   Zheng, Yuanjie ^a   Wei, Benzheng ^c   Bi, Hongsheng ^d,e   Wu, Jianfeng ^c   Pan, Xuemei ^e   Yin, Yilong ^d   Zhang, Shaoting ^b  

^a SHANDONG NORMAL UNIVERSITY   (China)

^b University of North Carolina at Charlotte   (United States)

^c SHANDONG UNIVERSITY OF TRADITIONAL CHINESE MEDICINE   (China)

^d SHANDONG UNIVERSITY   (China)

^e Eye Hospital Affiliated to Shandong University of Traditional Chinese Medicine   (China)

Author keywords

Choroid; CNN; Image segmentation; Learning; OCT

Indexed keywords

CONVOLUTION; DEEP NEURAL NETWORKS; IMAGE SEGMENTATION; NETWORK ARCHITECTURE; NEURAL NETWORKS; SCALES (WEIGHING INSTRUMENTS); TOMOGRAPHY;

CHOROID; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; GLOBAL INFORMATIONS; LEARNING; PATHOPHYSIOLOGIC; RETINAL STRUCTURE; STATE OF THE ART;

OPTICAL TOMOGRAPHY;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHOROID; CHOROIDAL THICKNESS; CLINICAL ARTICLE; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; HUMAN; IMAGE SEGMENTATION; LEARNING; OPTICAL COHERENCE TOMOGRAPHY; OPTICAL COHERENCE TOMOGRAPHY DEVICE; TWO-DIMENSIONAL IMAGING;

EID: 85011310997     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2017.01.023     Document Type: Article

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