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.

Methods are provided for measuring real-time concussive and vascular brain injuries, within seconds of the incident, by using a cascading decrease of optic nerve oxygen perfusion as captured by the retina oximetry, which is representative of inherent processes taking place within the brain subsequent to various degrees of cranial insult. The methods presented herein provide for establishing baseline databases and protocols that are intended to set standards for testing and protecting athletes on the field of play and creating emergency guidelines for early treatment of cerebrovascular accidents. Further, methods of investigation presented herein are capable of creating a database of demographically representative values, that are intended to provide statistical norms significant to demographics, such as age, sex, race, location, and athletic endeavors both at rest and within an active athletic state.

Examples relate to a surgical microscope system, and to a system, a method and a computer program for a surgical microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain intraoperative sensor data of at least a portion of an eye from a Doppler-based imaging sensor of the surgical microscope system. The system is configured to process the intraoperative sensor data to determine information on a blood flow within the eye. The system is configured to generate a visualization of the blood flow. The system is configured to provide a display signal to a display device of the surgical microscope system based on the visualization of the blood flow within the eye.

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.

A method for determining a calibration factor in Doppler flowmetry velocity measurements in the living eye includes imaging the eye with Doppler flowmetry and processing data to obtain blood velocity, volume, and flow maps using Doppler flowmetry formulas that provide velocity as a mean frequency expressed in Hz, and volume and flow in arbitrary units. A selected blood vessel is probed with Doppler OCT to measure the absolute velocity of blood at that location expressed in mm/s to determine a calibration factor used to convert the velocity measured with Doppler flowmetry expressed in Hz to velocity expressed in mm/s.

Systems and methods are provided for performing optical coherence tomography angiography for the rapid generation of en face images. According to one example embodiment, differential interferograms obtained using a spectral domain or swept source optical coherence tomography system are convolved with a Gabor filter, where the Gabor filter is computed according to an estimated surface depth of the tissue surface. The Gabor-convolved differential interferogram is processed to produce an en face image, without requiring the performing of a fast Fourier transform and k-space resampling. In another example embodiment, two interferograms are separately convolved with a Gabor filter, and the amplitudes of the Gabor-convolved interferograms are subtracted to generate a differential Gabor-convolved interferogram amplitude frame, which is then further processed to generate an en face image in the absence of performing a fast Fourier transform and k-space resampling. The example OCTA methods disclosed herein are shown to achieve faster data processing speeds compared to conventional OCTA algorithms.

The invention relates to an assembly (10) comprising an OCT device (20) for scanning an object region volume (22) arranged in an object region (18) using an OCT scanning beam (21), an object (24) with a section, which can be arranged in the object region (18) and which can be located in the object region volume (22) by means of the OCT device (20), in the object region volume (22), and a calculating unit (60) which is connected to the OCT device (20) and contains a computer program for ascertaining a 3D reconstruction of the object region volume (22) and for ascertaining the position of the section of the object (24) in the object region volume (22) by processing OCT scanning information obtained by scanning the object region volume (22) using the OCT device (20). According to the invention, the computer program has a calculation routine for ascertaining a target area (90) in the 3D reconstruction of the object region volume (22), said calculation routine determining a reference variable for the object (24) relative to the target area (90). The object (24) is designed as a surgical instrument which has a capillary with an opening for discharging a medium. The calculation routine of the computer program is used to ascertain an actual value of the volume of the medium discharged through the opening of the capillary in the target area by comparing data of the target area in the 3D reconstruction of the object region volume (22) and/or by comparing scanning information of the target area obtained by scanning the object region volume (22) using the OCT device (20) prior to and while discharging the medium. The invention also relates to a computer program and to a method for determining the volume of a medium discharged in an object region (18) through an opening by means of a surgical instrument with a capillary.

Embodiments disclosed herein generally relate to predicting geographic-atrophy lesion growth and/or geographic atrophy lesion size in an eye. The predictions can be generated by processing a data object using a neural network. The data object may include a three-dimensional data object representing a depiction of at least part of the eye or a multi-channel data object representing one or more decorresponding pictions of at least part of the eye. The neural network can include a convolutional multi-task neural network that is trained to learn features that are predictive of both lesion-growth and lesion-size outputs.

This disclosure relates generally to speckle image analysis, and, more particularly, to a system and method for imaging of localized and heterogenous dynamics using laser speckle. Existing speckle analysis techniques do not offer the capability to achieve both the dynamic phenomenon which carries over a specific time duration and localizing the extent of the activity at a single, chosen instant of time simultaneously. The present disclosure records an image stack consisting of N speckle images sequentially over a period, divides the image stack into a spatial window and a temporal window, converts the speckle intensity data comprised in the spatial window into a column vector. Construct a diagonal matrix and extract a singular value from the diagonal matrix, then defines a speckle intensity correlation metric using the plurality of singular values, defines a speckle activity and generates a speckle contrast image by graphically plotting the speckle activity values.

An optical coherence tomographic device may include: a light source; a measurement light generator that generates measurement light and generates reflected light from a target region in a scattering sample by irradiating the target region with the measurement light; a reference light generator that generates reference light; an interference light generator that generates interference light by combining the reflected light and the reference light; an interference light detector that detects the interference light and generates interference signals by converting the interference light; and a processor. The processor may cause the OCT device to execute: acquiring tomographic images for a same cross section in the target region in time series from the interference signals; calculating an entropy of the generated interference signals based on the tomographic images; and specifying a dynamic part in the tomographic images based on the calculated entropy.