Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images.
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Automatic and robust segmentation of endoscopic OCT images and optical staining.Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes.Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images.Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers.Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography
P2860
Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Length-adaptive graph search f ...... l coherence tomography images.
@en
Length-adaptive graph search f ...... l coherence tomography images.
@nl
type
label
Length-adaptive graph search f ...... l coherence tomography images.
@en
Length-adaptive graph search f ...... l coherence tomography images.
@nl
prefLabel
Length-adaptive graph search f ...... l coherence tomography images.
@en
Length-adaptive graph search f ...... l coherence tomography images.
@nl
P2093
P2860
P1476
Length-adaptive graph search f ...... al coherence tomography images
@en
P2093
Brenton Keller
David Cunefare
Joseph A Izatt
Tamer H Mahmoud
P2860
P356
10.1117/1.JBO.21.7.076015
P577
2016-07-01T00:00:00Z