In one embodiment, a patient's brain is evaluated after onset of a stroke by capturing computed tomography angiography (CTA) images of the brain, analyzing the CTA images with a CTA image analysis program to evaluate the patient's brain, and generating results based upon the analysis that provide an assessment of the brain. In some cases, the CTA image analysis program comprises a machine-learning algorithm that has been trained on the results of perfusion imaging analysis.

A system and method for automatically monitoring a ruminant animal. The system includes a 3D camera system that obtains images from a region of interest, in particular the paralumbar fossa. An image processor determines the surface curvature in the region of interest, as a function of time. Based on the frequency with which this function attains local maxima, a health indication for the animal is generated.

A system and method for augmenting an endoscopic display during a medical procedure including capturing a real-time image of a working space within a body cavity during a medical procedure. A feature of interest in the image is identified to the system using a user input handle of a surgical robotic system, and a graphical tag is displayed on the image marking the feature.

The present invention provides a computer-implemented method for analysing cell culture image data, in particular for analysing the spatiotemporal behaviour of the occurrence of a cellular event in a cell culture. The method comprise obtaining image data comprising a set of images of the cell culture at a plurality of consecutive time points, using an image analysis algorithm to determine the position of cells in at least a first population of cells in each of the images, linking at least some of the positions of cells in the images into respective cell tracks, and identifying the timing and position of occurrence of a cellular event in each of a plurality of cell tracks. The method may also comprise obtaining a spatiotemporal map of the events that have been identified, and quantifying the spatiotemporal pattern of occurrence of the event.

Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Apparatus, systems and methods for measuring changes in the volume of a tissue of a person are described herein. The methods include applying a first marker to a portion of a tissue, the first marker having a pattern of markings thereon, each marking of the pattern of markings having an initial position; capturing an image of the pattern of markings at a first time after applying the first marker to the tissue, at least one marking of the pattern of markings having a subsequent position at the first time when the image is captured; determining a change of position of the at least one marking by comparing the subsequent position of the at least one marking in the captured image to the initial position of the at least one marking; and based on the change of position of the at least one marking, determining the physiological measurement of the tissue.

An image processing apparatus is configured to determine whether a person in an image is a living body. The image processing apparatus includes a processor, and a memory storing executable instructions which, when executed by the processor, cause the image processing apparatus to perform operations including determining, based on a time-series image acquired during a predetermined time period, a threshold indicating a skin color range, obtaining color information describing a skin color area in the time-series image based on the threshold, and controlling, based on the color information, whether to continue biometric determination processing for determining whether a person in the time-series image is a living body.

Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW′ matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination. The present disclosure combines DTW distance information to weaken the dynamic change of functional connectivity and the influence of asynchrony of functional signals in different brain regions on the functional connectivity matrix, so that the calculated functional connectivity matrix can better reflect the correlation between the functional signals in different brain regions.

Systems and methods are described for diagnosing, assessing, and quantifying brain injuries, traumas, or concussions. The systems and methods described herein are non-invasive and based on the detection, measurement, and analysis of involuntary micromotions and/or fixational eye movements with respect to an individual's eyes, pupils, and head. Determinations as to brain injury are based, in part, on divergences between measurements associated with the subject and measurements contained in one or more datasets. The described systems and methods are accessible to subjects regardless of geographic location or access to medical professionals, and are not reliant on the cooperation of the subject such that the systems and methods described here are still effective when the subject is non-responsive.

The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.