A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry is configured to acquire time-series medical images including blood vessels of an examination subject, the time-series medical images being fluoroscopically captured in at least one direction at a plurality of points in time, generate a blood vessel shape model including time-series variation information about the blood vessels in an analysis region of the blood vessels on the basis of the acquired time-series medical images, and perform fluid analysis of blood flowing through the blood vessels on the basis of the generated blood vessel shape model.

A medical image processing device includes circuitry configured to: analyze a blood flow flowing in an observation object based on a medical observation image obtained by capturing an image of the observation object; and generate a difference result between a simulation result of a blood flow flowing in a 3D model acquired in advance for the observation object and an analysis result of a blood flow flowing in the observation object.

A platform for evaluating medical information and method for using the same are described. In one embodiment, the method comprises: monitoring, by a medical image management system, for a first indication of a content change at one or more data sources; determining, in response to the first indication, which of a plurality of image analysis engines is to analyze at least one image of one or more new medical images associated with the content change based on one or both of information accompanying the one or more images or results of applying body part detection on the at least one image; sending a first notification to start image analysis on the at least one image of the one or more medical images, the first notification being sent to each image analysis engine in a set of one or more image analysis engines determined to analyze the at least one image of the one or more new medical images; receiving a second indication that image analysis results are available from the set of one or more image analysis engines; and sending a second notification to subscribers to indicate availability of image analysis results for access and display thereby.

A jump counting method for jump rope is provided. The jump counting method comprises: S1, obtaining an original video data of a jump rope movement, and extracting an audio data and an image data from the original video data; S2, calculating the number of jumps of the rope jumper according to an audio information and an image information extracted from the audio data and the image data; and S3, outputting and displaying the calculation result.

Systems and methods for performing a medical imaging analysis task using a machine learning based motion model are provided. One or more medical images of an anatomical structure are received. One or more feature vectors are determined. The one or more feature vectors are mapped to one or more motion vectors using the machine learning based motion model. One or more deformation fields representing motion of the anatomical structure are determined based on the one or more motion vectors and at least one of the one or more medical images. A medical imaging analysis task is performed using the one or more deformation fields.

A method and system for registration of a multi-dimensional image include defining an input image and a reference image in the same fixed Cartesian grid, then mapping locations within the reference image and the input image to phase space using a Hamiltonian function to define a symplectomorphic map, where the map is embedded in an energy shell. The mapping step is iterated until a sequence of energy shells is created. The energy shells are used generate curvilinear mapping grid which is then applied to the first image to generate a registered output image data. The data may be preconditioned to account for nearest neighbor coupling and/or to equalize the dimensionality of the images.

Detecting breast abnormalities includes receiving a first mammographic image having original pixels. A second mammographic image is generated by enhancing the first mammographic image. Enhancing the first mammographic image includes performing the following for each original pixel in at least a subset of the original pixels. A histogram is generated for a region surrounding the original pixel, the region defined by an enhancement sliding window. Using the histogram, a value of the original pixel is revised to obtain a revised value, and the revised value is stored in the second mammographic image. A breast abnormality location is detected based on the second mammographic image.

Some embodiments are directed to a method of estimating a velocity of a contrast agent. The method includes receiving a plurality of video frames that were produced using a dynamic contrast enhanced imaging process, each video frame including a plurality of pixels/voxels. Information from the video frames is used to estimate velocity vectors indicating the velocity and direction of the agent with the vascular networks. The estimated velocity can be used to diagnose cancer, such as prostate cancer. Instead of velocity vectors, agent trajectories can be determined also used for the same purpose.

Methods and systems for characterizing fluids from a patient are disclosed. The method includes receiving a time series of images of a conduit receiving fluids from the patient, identifying a conduit image region in each of the images, classifying a flow type through the conduit based on an evaluation of the conduit image region in the time series of images, and estimating at least one of a volume of fluids and a quantity of a blood component that has passed through the conduit within a predetermined period of time, based at least in part on the classification of the flow type.

A method and associated system. Such is for orthodontic treatment or for assessing shape of an aligner. Within an example, there is participation in communication of an assessment of the suitability of an aligner as a function of the value of: a tooth attribute of an analysis tooth zone of an analysis image, the tooth attribute relating to a separation between the tooth represented by the analysis tooth zone and the aligner represented on the analysis image, or an image attribute of an analysis image, the image attribute relating to a separation between at least one tooth represented on the analysis image and the aligner represented on the analysis image. The analysis image is an image of an aligner in a service position in which it is worn by the patient acquired by a cellphone more than 1 week, and is a photograph, or an image extracted from a film.