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 filing followed by segmentation using a kernel graph-cut approach. A statistically derived mask is applied to the resulting scan creating an ROI 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.
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Jing Wu, Sebastian M. Waldstein, Bianca S. Gerendas, Georg Langs, Christian Simader, Ursula Schmidt-Erfurth, "Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion," Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133D (20 March 2015);