Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Improved optical coherence tomography systems and methods to measure thickness of the retina are presented. The systems may be compact, handheld, provide in-home monitoring, allow the patient to measure himself or herself, and be robust enough to be dropped while still measuring the retina reliably.

An image processing method performed by a processor and including detecting positions of plural vortex veins in a fundus image of an examined eye, and computing a center of distribution of the plural detected vortex vein positions.

A alteration caused in a nerve fiber layer could not accurately be displayed. There is provided a tomographic imaging apparatus including a generation means for generating a nerve fiber bundle map, a designation means for designating an arbitrary nerve fiber bundle in the nerve fiber bundle map, and a display control means for causing display means to display a parameter of the designated nerve fiber bundle.

Provided is an image processing apparatus configured to process an image of a fundus of an eye to accurately measure thicknesses of membranes that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points; and a measuring position acquiring unit configured to identify a measuring position regarding the wall of the blood vessel based on a position of the identified cell.

Provided is an image processing apparatus configured to process an image of a fundus of an eye, which is capable of simply and accurately measuring a distribution of cells that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; and a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points.

Improved optical coherence tomography systems and methods to measure thickness of the retina are presented. The systems may be compact, handheld, provide in-home monitoring, allow the patient to measure himself or herself, and be robust enough to be dropped while still measuring the retina reliably.

Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.

An ophthalmologic apparatus includes an optical scanner, an interference optical system, an intraocular distance calculator, an image correcting unit, and a controller. The optical scanner is disposed at an optically substantially conjugate position with a first site of a subject's eye. The interference optical system is configured to split light from a light source into reference light and measurement light, to project the measurement light onto the subject's eye via the optical scanner, and to detect interference light between returning light of the light from the subject's eye and the reference light via the optical scanner. The image forming unit is configured to form a tomographic image of the subject's eye corresponding a first traveling direction of the measurement light deflected by the optical scanner, based on a detection result of the interference light. The intraocular distance calculator is configured to obtain an intraocular distance between predetermined sites of the subject's eye based on the detection result of the interference light. The image correcting unit is configured to correct the tomographic image based on the intraocular distance. The controller is configured to control at least the optical scanner.

Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.