A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes.
about
In vivo imaging methods to assess glaucomatous optic neuropathyAutomated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice.Automatic Three-dimensional Detection of Photoreceptor Ellipsoid Zone Disruption Caused by Trauma in the OCT.Voxel Based Morphometry in Optical Coherence Tomography: Validation & Core FindingsAutomated Internal Classification of Beadless Chinese ZhuJi Fleshwater Pearls based on Optical Coherence Tomography Images.Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.Longitudinal graph-based segmentation of macular OCT using fundus alignment.Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology.Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images.Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes.Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images.Automatic segmentation of microcystic macular edema in OCT.Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images
P2860
Q28081361-B071A585-D067-417F-AF2B-45426CE22FB7Q34292418-FFCE2A23-2D01-41B6-B3D5-15F1E2574956Q36881802-B04FC316-AF5B-4FC9-9569-7EB76D6579A7Q36908647-E5C075C8-E5D2-443F-80C4-EF865999BB15Q37281937-2A12F8D8-928E-446B-9BE6-747E73033F63Q38619951-B534DEC1-157D-4AA4-90B0-D362B8EDCC49Q39699517-23A9FCF3-30D8-476C-B9B8-E817F7E45D8FQ40891150-D0BFA2C1-D0AF-4ADC-8238-B4609E01BF60Q41946325-159AF9E3-E6B4-487C-98DC-C7389274C085Q42274559-705B234B-9A0B-4A1A-837E-7139725ED0E2Q42565216-216B3EDE-FFAA-445E-8CA8-3A3E13A68D12Q42956754-12C6CF58-C2BE-46DC-8AD6-70A0AE12636CQ43131929-BAF481F1-EA3A-4365-B19C-0346A32189BFQ58692979-3D5C6AAF-9DA6-40D2-9AF7-ACE016EF06C9
P2860
A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes.
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年学术文章
@wuu
2013年学术文章
@zh-cn
2013年学术文章
@zh-hans
2013年学术文章
@zh-my
2013年学术文章
@zh-sg
2013年學術文章
@yue
2013年學術文章
@zh
2013年學術文章
@zh-hant
name
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@en
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@nl
type
label
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@en
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@nl
prefLabel
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@en
A combined machine-learning an ...... al surfaces in SD-OCT volumes.
@nl
P2860
P50
P356
P1476
A combined machine-learning an ...... nal surfaces in SD-OCT volumes
@en
P2093
Mona K Garvin
Woojin Jeong
P2860
P304
P356
10.1364/BOE.4.002712
P577
2013-11-01T00:00:00Z