Carl Zeiss Meditec Ag

Carl Zeiss Meditec Gmbh

Warren Lewis

Mary K Durbin

Luis De Sisternes

Sophie Kubach

Lars Omlor

Aaron Lee

Theodore Spaide

Arindam Bhattacharya

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

A system and/or method uses a trained U-Net neural network to remove flow artifacts from optical coherence tomography (OCT) angiography (OCTA) data. The trained U-Net receives as input both OCT structural volume data and OCTA volume data, but expands the OCTA volume data to include depth information. The U-Net applies dynamic pooling along the depth direction and weighs more heavily the portion of the data that follows (e.g., along the contours of) select retinal layers. In this manner the U-Net applies contextually different computations at different axial locations based at least in part on the depth index information and/or (e.g., proximity to) the select retinal layers. The U-net outputs OCT volume data of reduced flow artifacts as compared with the input OCTA data.

Correction of flow projection artifacts in octa volumes using neural networks

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Yellow Springs, OH, United States of America

Warren Lewis