Paper Info
Title
Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion
Abstract
Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high-resolution, three-dimensional (3D) cross-sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD), glaucoma and retinal vein occlusion (RVO).(1) Disease diagnosis, assessment, and treatment will require a patient to undergo multiple OCT scans, possibly using multiple scanners, to accurately and precisely gauge disease activity, progression and treatment success. However, cross-vendor imaging and patient movement may result in poor scan spatial correlation potentially leading to incorrect diagnosis or treatment analysis. The retinal fovea is the location of the highest visual acuity and is present in all patients, thus it is critical to vision and highly suitable for use as a primary landmark for cross-vendor/cross-patient registration for precise comparison of disease states. However, the location of the fovea in diseased eyes is extremely challenging to locate due to varying appearance and the presence of retinal layer destroying pathology. Thus categorising and detecting the fovea type is an important prior stage to automatically computing the fovea position. Presented here is an automated cross-vendor method for fovea distinction in 3D SD-OCT scans of patients suffering from RVO, categorising scans into three distinct types. OCT scans are preprocessed by motion correction and noise filtering followed by segmentation using a kernel graph-cut approach. A statistically derived mask is applied to the resulting scan creating an ROT around the probable fovea location from which the uppermost retinal surface is delineated. For a normal appearance retina, minimisation to zero thickness is computed using the top two retinal surfaces. 3D local minima detection and layer thickness analysis are used to differentiate between the remaining two fovea types. Validation employs ground truth fovea types identified by clinical experts at the Vienna Reading Center (VRC). The results presented here are intended to show the feasibility of this method for the accurate and reproducible distinction of retinal fovea types from multiple vendor 3D SD-OCT scans of patients suffering from RVO, and for use in fovea position detection systems as a landmark for intra- and cross-vendor 3D OCT registration.
Year
DOI
Venue
2015
10.1117/12.2076570
Proceedings of SPIE
Keywords
Field
DocType
automated fovea distinction,cross-vendor,3D SD-OCT,retinal disease,multimodal registration
Computer vision,Glaucoma,Optical coherence tomography,Visual acuity,Fovea centralis,Retina,Optics,Artificial intelligence,Macular degeneration,Retinal,Physics,Retinal Vein
Conference
Volume
ISSN
Citations 
9413
0277-786X
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Jing Wu182.58
Sebastian Waldstein2808.52
Bianca Gerendas3245.67
Georg Langs464857.73
Christian Simader5103.00
Ursula Schmidt-Erfurth69011.43