Carl Zeiss Meditec Gmbh

Carl Zeiss Meditec Ag

Sophie Kubach

Mary K Durbin

Utkarsh Sharma

Lin An

Luis De Sisternes

Matthew J Everett

Lars Omlor

Conor Leahy

Jennifer Y Luu

Gregory G Anderson

Warren Lewis

Katherine Makedonsky

Simon Bello

Arindam Bhattacharya

Ali Fard

Kevin Meng

Patrick Krawec

An OCT system includes a machine learning (ML) model trained to receive a single OCT scan/image and provide an image translation and/or denoise function. The ML model may be based on a neural network (NN) architecture including a series of encoding modules in a contracting path followed by a series of decoding modules in an expanding path leading to an output convolution module. An intermediate error module determines a deep error measure, e.g., between a training output image and at least one encoding module and/or decoding module, and an error from the output convolution module is combined with the deep error measure. The NN may be trained using true averaged images as ground truth, training outputs. Alternatively, the NN may be trained using randomly selected, individual OCT images/scans as training outputs.

System for OCT image translation, ophthalmic image denoising, and neural network therefor

After OCT scans are acquired, error compensation is be applied anew by identifying motion tracking data that was collected simultaneously with (or closest in time to) each acquired OCT scan. The effects of any previously applied motion tracking information is removed from an OCT scan before applying the new motion tracking data, which may provide higher resolution motion tracking.

Post-processing method to improve LSO-based tracking in oct

An ophthalmic imaging system provides a user interface to facilitate the montaging of scan images collected with various imaging modalities, such as images collected with a fundus imaging system or an optical coherence tomography (OCT) system. The amount of each constituent image used in the montage is dependent upon its respective quality. During the collecting of scans (constituent images) for montaging, any scan may be designated for rescanning, such as if its current quality is deemed less than sufficient. In the case of using an OCT system to collect constituent images (e.g., cube scans), the scanned region of a constituent image may be modified based on physical characteristics of the eye being scanned.

Systems and methods for improved montaging of ophthalmic imaging data

Methods for improved acquisition and processing of optical coherence tomography (OCT) angiography data are presented. One embodiment involves improving the acquisition of the data by evaluating the quality of different portions of the data to identify sections having non-uniform acquisition parameters or non-uniformities due to opacities in the eye such as floaters. The identified sections can then be brought to the attention of the user or automatically reacquired. In another embodiment, segmentation of layers in the retina includes both structural and flow information derived from motion contrast processing. In a further embodiment, the health of the eye is evaluating by comparing a metric reflecting the density of vessels at a particular location in the eye determined by OCT angiography to a database of values calculated on normal eyes.

Acquisition and analysis techniques for improved outcomes in optical coherence tomography angiography

Acquistion and analysis techniques for improved outcomes in optical coherence tomography angiography

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Menlo Park, CA, United States of America

Sophie Kubach