Optical Coherence Tomography (OCT) is a non-invasive imaging technique based on light of short coherence length. OCT works on the principle of ultrasound imaging, using light instead of sound waves. Interferometry is used to measure differences in transit time between the backscattered light of the sample beam and the light of the reference beam, which allows three-dimensional volume images with micrometer resolution.

Deep learning methods for improved monitoring of age-related macular degeneration in time-spatial 4D-OCT image sequences

In this project deep learning based learning methods for an improved monitoring of age-related macular degeneration (AMD) are developed. The project is carried out in cooperation with the Clinic for Ophthalmology of the University Medical Center Schleswig-Holstein in Kiel and the Institute for Biomedical Optics of the University of Lübeck.

Age-related macular degeneration (AMD) is the most common cause of blindness at the age of 60 and older in the western world. In the wet, exudative form of AMD an edema formation under the retina occurs. Due to an oxygen deficiency in the photoreceptors, messenger substances such as the Vascular Endothelial Growth Factor (VEGF) are formed, which leads to choroidal neovascularization. The newly formed vessels are very fragile, causing unwanted bleeding in the macula. In order to counteract the uncontrolled blood vessel growth, VEGF antibodies can be injected into the vitreous cavity. However, the therapeutic effect only lasts for a few weeks, so that a Reiinjektion is necessary after this period. To improve the diagnosis and therapy of AMD, the project will develop, implement and validate new deep learning methods for computer-aided analysis and detection of biomarkers (Fig. 1) in spatio-temporal 4D-OCT image data. The developed methods are intended to detect and classify the biomarkers in the OCT image data so that a recurrence can be detected early.