A computer-implemented method, an apparatus, and a system for estimating synthetic values of quantitative metrics are provided. They involve calculating new, more accurate boundaries using a classifier based on local intensity and spatial estimators, for the segmentation mask provided by a non-local means patch-based segmentation in a test image, and estimating for the pixels of interest at least one synthetic value of a quantitative metric using a given value of the quantitative metric assigned to the reference images and the boundaries. The method, apparatus, and system provide the advantage of generating synthetic values directly comparable against known values for given subjects or against predetermined scales for diagnostic or prognostic purposes. In the specific case of Alzheimer's disease, the invention stretches the predictive range up to two full decades, which constitutes a significant advance in the field of medical diagnostics.

The present invention provides a physiological information detection method for calculating a physiological value by using changes of a dynamic image. The detection method includes: acquiring detection data from a gray-scale value of the dynamic image, and transforming the detection data into frequency data. The detection method further includes: determining whether the frequency data meet a preset condition, and using a transformation model of a corresponding transformation combination accordingly to transform the frequency data into a physiological value. The present invention further provides a physiological information detection device applying the detection method.

A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.

A technology capable of estimating information on a position and a size of a pupil or an iris using an image obtained by photographing eyes of a subject even when a part of the pupil or the iris is hidden in the image is provided. An eye information estimate apparatus includes a profile determination information acquisition unit configured to acquire, from an image obtained by photographing an eye of a subject, coordinates (x1, y0) and (x2, y0) of two points of a point P1 and a point P2 respectively corresponding to an outer edge of a pupil or an iris on a predetermined line included in the image and, in a case when a shape of the pupil or the iris is assumed to be an ellipse, slopes θ1 and θ2 of tangent lines of the ellipse at the point P1 and the point P2 respectively from the image, and an eye information calculation unit configured to calculate, R being set a length of a major axis of the ellipse, center coordinates (xc, yc) and an angle of rotation ψ of the ellipse representing a position of the pupil or the iris, and/or a length Rb of a minor axis of the ellipse representing a size of the pupil or the iris using the coordinates (x1, y0) and (x2, y0) of the point P1 and the point P2 respectively, the slopes θ1 and θ2 of the tangent lines of the ellipse at the point P1 and the point P2 respectively, and the length R of the major axis of the ellipse.

The present invention relates to a device (1) for fractional flow reserve determination. The device (1) comprises a model generator (10) configured to generate a three-dimensional model (3DM) of a portion of an imaged vascular vessel tree (VVT) surrounding a stenosed vessel segment (SVS), based on a partial segmentation of the imaged vascular vessel tree (VVT). Further, the device comprises an image processor (20) configured to calculate a blood flow (Q) through the stenosed vessel segment (SVS) based on an analysis of a time-series of X-ray images of the vascular vessel tree (VVT). Still further, the device comprises a fractional-flow-reserve determiner (30) configured to determine a fractional flow reserve (FFR) based on the three-dimensional model (3DM) and the calculated blood flow.

Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.

In an approach to automatic detection of vertebral dislocations, training data is received, where the training data includes an ordered sequence of radiographic image patches of vertebrae and intervertebral spaces, and location data for each vertebra and each intervertebral space. Location deep learning models are trained to detect a location of each vertebra and each intervertebral space from the training data. Classification deep learning models are trained to classify an ordered sequence of image patches to identify vertebral anomalies of the vertebrae and the intervertebral spaces from the training data. Responsive to receiving radiographic image files, the location deep learning models and the classification deep learning models are applied to the radiographic image files to create a condition assessment.

The invention discloses an apparatus (100) for processing a medical image associated with a subject. The apparatus comprises a clinical information extractor (102) for determining clinical information associated with the medical image; a plurality of image processors (104), each image processor for performing at least one image processing task in respect of the medical image; and an image processing manager (106) for determining, based at least on the determined clinical information associated with the medical image, at least one image processor of the plurality of image processors to perform one or more tasks in respect of the medical image.

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 and an overlay is displayed on the image marking the feature. Computer vision is used to detect in real time changes in the image that are indicative of the feature of interest being at least partially obscured. In response to such detected changes, a quality of the overlay is altered, e.g. to make it more bright or opaque, based on the change in visibility of the feature of interest in the image.

Embodiments of the present application provides a method and a system for evaluating implanting precision of an implant. The method includes: obtaining a second surface model of an oral cavity containing a target implant; obtaining an actual position of the target implant in a first three-dimensional model based on the second surface model, the first three-dimensional model and a first surface model; and obtaining an evaluation result of the implanting precision of the target implant based on the actual position of the target implant in the first three-dimensional model and an predicted position of the target implant in the first three-dimensional model. By means of the method and system according to the embodiments of the present application, the second surface model of the oral cavity containing the target implant is obtained after implantation is completed using an oral digital scanning device to obtain the evaluation result of the implanting precision of an implant of the target implant. More accurate evaluation result of implant can be obtained due to the elimination of metallic implant artifacts in the obtained secondary surface model as compared to the three-dimensional digital model obtained based on CT device in the prior art.