Abstract: System/Method/Device for labelling images in an automated manner to satisfy a performance of a different algorithm and then applying active learning to learn a deep learning model which would enable ‘real-time’ operation of quality assessment and with high accuracy.

Abstract: A system and method for use with optical coherence tomography (OCT) data to identify a target pathology extracts multiple pathology-characteristic images from the OCT data. The extracted pathology-characteristic images may include a mixture of OCT structural images (including retinal layer thickness information) and OCT angiography images. Optionally, other pathology-characteristic images and data maps (mapped to corresponding positions in the OCT data), such as fundus images and visual field test maps may be accessed as additional pathology-characteristic images. Each pathology-characteristic image defines a different image channel (e.g., “color channel”) per pixel in a composite, channel-coded image, which is then used to train a neural network to search for the target pathology in OCT data. The trained neural network may then receive new composite, channel-coded image and identify/segment the target pathology within the new channel-coded image.

Abstract: 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.

Abstract: 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.