Christian Simader

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Patient movements during the acquisition of SD-OCT scans create substantial motion artefacts in the volumetric data that hinder registration and 3D analysis and can be mistaken for pathologies. In this paper we propose a method to correct these artefacts using a single volume scan while still retaining the overall shape of the retina. The method was(More)
We propose a method to predict treatment response patterns based on spatio-temporal disease signatures extracted from longitudinal spectral domain optical coherence tomography (SD-OCT) images. We extract spatio-temporal disease signatures describing the underlying retinal structure and pathology by transforming total retinal thickness maps into a joint(More)
BACKGROUND/AIMS To determine the reproducibility among readers of two independent certified centres, the Vienna Reading Center (VRC) and the University of Wisconsin-Madison Reading Center (UW-FPRC) for optical coherence tomography (OCT) images in age-related macular degeneration (AMD). METHODS Fast macular thickness scans and 6 mm cross hair scans(More)
Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not(More)
BACKGROUND AND OBJECTIVES The lack of benchmark data in computational ophthalmology contributes to the challenging task of applying disease assessment and evaluate performance of machine learning based methods on retinal spectral domain optical coherence tomography (SD-OCT) scans. Presented here is a general framework for constructing a benchmark dataset(More)
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