scholarly journals Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162001 ◽  
Author(s):  
Louise Terry ◽  
Nicola Cassels ◽  
Kelly Lu ◽  
Jennifer H. Acton ◽  
Tom H. Margrain ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Layer Segmentation

A Level Set Method with Region-Scalable Fitting Energy for Retinal Layer Segmentation in Spectral-Domain Optical Coherence Tomography Images

2020 ◽  
Vol 10 (2) ◽  
pp. 326-335
Author(s):  
Liming Liang ◽  
Xiaoqi Sheng ◽  
Bowen Liu ◽  
Zhimin Lan
Keyword(s):  
Optical Coherence Tomography ◽  
Level Set ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Spectral Domain ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Retinal Layers ◽  
Layer Segmentation ◽  
Sd Oct

Retinal layer segmentation of spectral-domain optical coherence tomography images plays an important role during diagnosis and analysis of ophthalmic diseases. In this paper, a novel variational level set framework with region-scalable fitting energy is proposed for automated retinal layer segmentation in SD-OCT. To the best of our knowledge, it is the first time that level set based method succeeds in ten retinal layers segmentation. The proposed framework consists of three steps. First, an anisotropic nonlinear diffusion filter is applied for speckle noise reduction and ROI contrast enhancement. Second, Canny edge detectors are used to extract initial layers: nerve fiber layer, connecting cilia and retinal pigment epithelium. Finally, the rest retinal layers are segmented by means of level set model combined with prior knowledge of retinal thickness and morphology, for which the energy function consists of region-scalable fitting energy data term, area constraint term, regularization term and length penalty term. The proposed method was tested on 50 retinal SD-OCT B-scans from 50 normal subjects. The overall unsigned border position error is 5.92 ± 4.72 μm. The result showed that data terms with border weight terms can keep layer segmentation results in strong border while retaining its fitting capability in weak border. The proposed method achieves better segmentation result than other active contour models.

scholarly journals Fast retinal layer segmentation of spectral domain optical coherence tomography images

2015 ◽  
Vol 20 (9) ◽  
pp. 096014 ◽  
Author(s):  
Tianqiao Zhang ◽  
Zhangjun Song ◽  
Xiaogang Wang ◽  
Huimin Zheng ◽  
Fucang Jia ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Layer Segmentation

Retinal Layer Segmentation in Patients with Multiple Sclerosis Using Spectral Domain Optical Coherence Tomography

Ophthalmology ◽  
2014 ◽  
Vol 121 (2) ◽  
pp. 573-579 ◽  
Author(s):  
Elena Garcia-Martin ◽  
Vicente Polo ◽  
Jose M. Larrosa ◽  
Marcia L. Marques ◽  
Raquel Herrero ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Layer Segmentation

scholarly journals Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

2009 ◽  
Vol 28 (9) ◽  
pp. 1436-1447 ◽  
Author(s):  
M.K. Garvin ◽  
M.D. Abramoff ◽  
Xiaodong Wu ◽  
S.R. Russell ◽  
T.L. Burns ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Layer Segmentation

scholarly journals An approach to automated retinal layer segmentation in SDOCT images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 56
Author(s):  
Mohandass G ◽  
Hari Krishnan G ◽  
Hemalatha R J
Keyword(s):  
Optical Coherence Tomography ◽  
Imaging Technique ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Seed Point ◽  
Deformable Contour ◽  
Gradient Based ◽  
Oct Imaging ◽  
Layer Segmentation ◽  
Region Growth

The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise detection, i.e., from the Robust Outlyingness Ratio (ROR). It is then applied to edge and layer detection using a gradient-based deformable contour model. Second, the image is vectorised. In this method, a cluster and column intensity grid is applied to identify and determine the unsegmented layers. Using the layer intensity and a region growth seed point algorithm, segmentation of the prominent layers is achieved. The automatic BOR method is an image segmentation process that determines the eight layers in retinal spectral domain optical coherence tomography images. The highlight of the BOR method is that the results produced are accurate, highly substantial, and effective, although time consuming. 

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