Described herein are systems, methods, and instrumentalities associated with automatically detecting and enhancing multiple objects in medical scan images. The detection and/or enhancement may be accomplished utilizing artificial neural networks such as one or more classification neural networks and/or one or more graph neural networks. The neural networks may be used to detect areas in the medical scan images that may correspond to the objects of interest and cluster the areas belonging to a same object into a respective cluster. These tasks may be accomplished, for example, by representing the areas corresponding to the objects of interest and their interrelationships with a graph and processing the graph through the one or more graph neural networks so that the areas belonging to each object may be properly labeled and clustered. The clusters may then be used to enhance the objects of interests in one or more output scan images.

A method for vascular assessment is disclosed. The method includes receiving a plurality of medical images of a portion of a vasculature of a subject and processing the medical images to produce a model of the vasculature. The method further includes obtaining a flow characteristic of the model and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the model.

A medical image processing apparatus includes a processing circuitry. The processing circuitry obtains volume data including a tubular organ. The processing circuitry extracts the tubular organ from the volume data. The processing circuitry calculates each of a plurality of feature quantities at a plurality of positions in the tubular organ. The processing circuitry calculates a graph indicating a distribution of the plurality of feature quantities at the plurality of positions. The processing circuitry displays the graph and the tubular organ on a display, the displayed graph being aligned with the displayed tubular organ.

Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parametrized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

Provided is a highly reliable technique for the evaluation of ischemic conditions. A preferred embodiment is a nuclear medicine measurement protocol in which the administration of a radiopharmaceutical agent and radiation measurement are performed twice at rest and under stress. In the nuclear medicine measurement protocol, radiation collection is performed without radiopharmaceutical agent administration before the second radiopharmaceutical agent administration, and the result is used to correct the nuclear medicine measurement result after the second radiopharmaceutical agent administration.

Provided is a novel technique for quantitatively evaluating tracer accumulation for nuclear medicine examinations of the heart. In a preferred embodiment, the radiation count information obtained by myocardial nuclear medicine measurement is normalized using a value relating to the size of the heart. In a preferred embodiment, the pixel value of each pixel of myocardial nuclear medicine image data is converted into standardized uptake values (SUV) capable of being represented by [SUV=Tissue radioactivity concentration/(Administered radiation dose/Value relating to size of heart)]. The value relating to the size of the heart may be a myocardial weight, for example.

A system and method for automatically analyzing placenta insufficiency in a curved topographical ultrasound image slice is provided. The method includes acquiring, by an ultrasound system, an ultrasound volume of a placental anatomy section, the ultrasound volume comprising color Doppler information. The method includes extracting, by at least one processor of the ultrasound system, a topographical ultrasound image slice at a distance below an inner surface of the placental anatomy section. The topographical ultrasound image slice is curved in all three dimensions and includes the color Doppler information. The method includes analyzing, by the at least one processor, the color Doppler information of the topographical ultrasound image slice to generate perfusion data information. The method includes causing, by the at least one processor, a display system to present the topographical ultrasound image slice with the perfusion data information.

Embodiments of the disclosure provide an angiography image determination method and an angiography image determination device. The method includes: obtaining a plurality of first images of a body part injected with a contrast medium; obtaining a plurality of corresponding second images by performing a first image preprocessing operation on each first image; obtaining a pixel statistical characteristic of each second image; finding a candidate image based on the pixel statistical characteristic of each second image; and finding a reference image corresponding to the candidate image among the plurality of first images.

A method for estimating parameter values includes acquiring image data with an imaging apparatus, deriving a parameter model function from the image data, generating a N-dimensional grid, wherein N is a number of values of one or more non-linear terms of the derived model function, pre-calculating the one or more non-linear terms given the parameter model function and the designated values of the non-linear parameters, calculating one or more remaining model terms of the parameter model function, and displaying at least one of the one or more non-linear terms and remaining linear model terms.