Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images

2020 ◽  
Vol 29 ◽  
pp. 303-312 ◽  
Author(s):  
Lua Ngo ◽  
Jaepyeong Cha ◽  
Jae-Ho Han
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Layer Segmentation

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

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

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. 

Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography

2015 ◽  
Vol 26 (1) ◽  
pp. 146-158 ◽  
Author(s):  
Jelena Novosel ◽  
Gijs Thepass ◽  
Hans G. Lemij ◽  
Johannes F. de Boer ◽  
Koenraad A. Vermeer ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Level Sets ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Loosely Coupled ◽  
Layer Segmentation

scholarly journals Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasmine Derradji ◽  
Agata Mosinska ◽  
Stefanos Apostolopoulos ◽  
Carlos Ciller ◽  
Sandro De Zanet ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Degeneration ◽  
Vision Loss ◽  
Retinal Pigment ◽  
Retinal Disease ◽  
Age Related Macular Degeneration ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Age Related ◽  
Layer Segmentation

AbstractAge-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.

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