Retinal layer delineation through learning of tissue photon interaction in optical coherence tomography

Author(s):  
S. P. K. Karri ◽  
Niladri Garai ◽  
Debaleena Nawn ◽  
Sambuddha Ghosh ◽  
Debjani Chakraborty ◽  
...  
2016 ◽  
Vol 57 (9) ◽  
pp. OCT341 ◽  
Author(s):  
Justin Wanek ◽  
Norman P. Blair ◽  
Felix Y. Chau ◽  
Jennifer I. Lim ◽  
Yannek I. Leiderman ◽  
...  

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162001 ◽  
Author(s):  
Louise Terry ◽  
Nicola Cassels ◽  
Kelly Lu ◽  
Jennifer H. Acton ◽  
Tom H. Margrain ◽  
...  

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e035397
Author(s):  
Svenja Specovius ◽  
Hanna G Zimmermann ◽  
Frederike Cosima Oertel ◽  
Claudia Chien ◽  
Charlotte Bereuter ◽  
...  

PurposeOptical coherence tomography (OCT) captures retinal damage in neuromyelitis optica spectrum disorders (NMOSD). Previous studies investigating OCT in NMOSD have been limited by the rareness and heterogeneity of the disease. The goal of this study was to establish an image repository platform, which will facilitate neuroimaging studies in NMOSD. Here we summarise the profile of the Collaborative OCT in NMOSD repository as the initial effort in establishing this platform. This repository should prove invaluable for studies using OCT to investigate NMOSD.ParticipantsThe current cohort includes data from 539 patients with NMOSD and 114 healthy controls. These were collected at 22 participating centres from North and South America, Asia and Europe. The dataset consists of demographic details, diagnosis, antibody status, clinical disability, visual function, history of optic neuritis and other NMOSD defining attacks, and OCT source data from three different OCT devices.Findings to dateThe cohort informs similar demographic and clinical characteristics as those of previously published NMOSD cohorts. The image repository platform and centre network continue to be available for future prospective neuroimaging studies in NMOSD. For the conduct of the study, we have refined OCT image quality criteria and developed a cross-device intraretinal segmentation pipeline.Future plansWe are pursuing several scientific projects based on the repository, such as analysing retinal layer thickness measurements, in this cohort in an attempt to identify differences between distinct disease phenotypes, demographics and ethnicities. The dataset will be available for further projects to interested, qualified parties, such as those using specialised image analysis or artificial intelligence applications.


2018 ◽  
Vol 7 (2.25) ◽  
pp. 56
Author(s):  
Mohandass G ◽  
Hari Krishnan G ◽  
Hemalatha R J

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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