Retinal-Layer Segmentation Using Dilated Convolutions

2019 ◽  
pp. 279-292
Author(s):  
T. Guru Pradeep Reddy ◽  
Kandiraju Sai Ashritha ◽  
T. M. Prajwala ◽  
G. N. Girish ◽  
Abhishek R. Kothari ◽  
...  
Keyword(s):  
Retinal Layer ◽  
Layer Segmentation

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

Automated Retinal Layer Segmentation Based on Optical Coherence Tomographic Images

2016 ◽  
Vol 36 (10) ◽  
pp. 1011003
Author(s):  
贺琪欲 He Qiyu ◽  
李中梁 Li Zhongliang ◽  
王向朝 Wang Xiangzhao ◽  
南楠 Nan Nan ◽  
卢宇 Lu Yu
Keyword(s):  
Retinal Layer ◽  
Optical Coherence ◽  
Tomographic Images ◽  
Layer Segmentation

Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation

2020 ◽  
Vol 106 ◽  
pp. 101871
Author(s):  
Qifeng Yan ◽  
Bang Chen ◽  
Yan Hu ◽  
Jun Cheng ◽  
Yan Gong ◽  
...  
Keyword(s):  
Super Resolution ◽  
Speckle Reduction ◽  
Retinal Layer ◽  
Layer Segmentation

Automated retinal layer segmentation in OCT images using spatially variant filtering

2008 ◽  
Author(s):  
Ahmet M. Bagci ◽  
Rashid Ansari ◽  
Mahnaz Shahidi
Keyword(s):  
Retinal Layer ◽  
Variant Filtering ◽  
Layer Segmentation ◽  
Spatially Variant

scholarly journals Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images

2018 ◽  
Vol 9 (9) ◽  
pp. 4509 ◽  
Author(s):  
Abhay Shah ◽  
Leixin Zhou ◽  
Michael D. Abrámoff ◽  
Xiaodong Wu
Keyword(s):  
Neural Nets ◽  
Retinal Layer ◽  
Surface Segmentation ◽  
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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