Carl Zeiss Meditec Ag

Carl Zeiss Meditec Gmbh

Niranchana Manivannan

Mary K Durbin

Matthew J Everett

Keith E O'Hara

Luis De Sisternes

Lars Omlor

Conor Leahy

David Nolan

Keith Brock

Priya Kulkarni

Jeffrey Schmidt

Michael Chen

An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

Patient tuned ophthalmic imaging system with single exposure multi-type imaging, improved focusing, and improved angiography image sequence display

An automated segmentation and identification system/method for identifying geographic atrophy (GA) phenotypic patterns in fundus autofluorescence images. A hybrid process combines a supervised pixel classifier with an active contour algorithm. A trained, machine learning model (e.g., SVM or U-Net) provides initial GA segmentation/classification, and this is followed by Chan-Vese active contour algorithm. The junctional zones of the GA segmented area are then analyzed for geometric regularity and light intensity regularity. A determination of GA phenotype is made, at least in part, from these parameters.

Segmentation and classification of geographic atrophy patterns in patients with age related macular degeneration in widefield autofluorescence images

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Fremont, CA, United States of America

Niranchana Manivannan