A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains image data rendering a blood vessel of a patient. The processing circuitry performs a fluid analysis on the obtained image data and calculates an index value related to a blood flow in the blood vessel with respect to each of a plurality of positions in the blood vessel. With respect to the index values to be calculated, the processing circuitry selects a position in which a first value is to be obtained from among the plurality of positions or selects a value serving as the first value from among the index values exhibited in positions. The processing circuitry causes a display to display the first value in a predetermined display region thereof used for displaying the first value.

A computer-implemented method for high resolution image inpainting comprising the following steps: providing a high resolution input image, providing at least one inpainting mask, selecting at least one rectangular sub-region of the input image and at least one aligned rectangular subregion of the inpainting mask such that the rectangular subregion of the input image encompasses at least one set of pixels to be removed and synthetized, the at least one sub-region of the input image and its corresponding aligned subregion of the inpainting mask having identical minimum possible size and a position for which a calculated information gain does not decrease, processing the sub-region of the input image and its corresponding aligned subregion of the inpainting mask by a machine learning model, generating an output high resolution image comprising the inpainted sub-region.

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

An image segmentation apparatus for magnetic resonance imaging according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a localizer image of an organ, the localizer image being three-dimensional or being in a plurality of layers and two-dimensional. The processing circuitry is configured to temporarily localize, on a basis of the localizer image, a segment in which the organ is present in terms of the layer direction of a plurality of slices included in the localizer image. The processing circuitry is configured to obtain a segmentation result of the organ, by performing an image segmentation process on the localizer image positioned inside the segment in which the organ is present.

Systems and methods can quantify the tumor microenvironment for diagnosis, prognosis and therapeutic response prediction by fusing different data types (e.g., morphological information from histology and molecular information from omics) using an algorithm that harnesses deep learning. The algorithm employs tensor fusion to provide end-to-end multimodal fusion to model the pairwise interactions of features across multiple modalities (e.g., histology and molecular features) and deep learning. The systems and methods improve upon traditional methods for quantifying the tumor microenvironment that rely on concatenation of extracted features.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Methods and systems for constructing a three-dimensional (3D) model of a user in a virtual environment for full body virtual reality (VR) applications are described. The method includes receiving an image of the user captured using an RGB camera; detecting a body bounding box associated with the user using a first trained neural network; determining a segmentation map of the user, based on the body bounding box; determining a two-dimensional (2D) contour of the user from the segmentation map; forming a 3D extrusion model by extruding the 2D contour; and constructing the 3D model of the user in the virtual environment by applying a geometric transformation to the 3D extrusion model. Applications of full body VR include physical training and fitness sessions, games, control of computing devices, manipulation and display of data, interactive social media with VR, and the like.

The present invention provides a method for quantitatively identifying the defects of large-size composite material based on infrared image sequence, firstly obtaining the overlap area of an infrared splicing image, and dividing the infrared splicing image into three parts according to overlap area: overlap area, reference image area and registration image area, then extracting the defect areas from the infrared splicing image to obtain P defect areas, then obtaining the conversion coordinates of pixels of defect areas according to the three parts of the infrared splicing image, and further obtaining the transient thermal response curves of centroid coordinate and edge point coordinates, finding out the thermal diffusion points from the edge points of defect areas according to a created weight sequence and dynamic distance threshold ε.sub.ttr×d.sub.p_max, finally, based on the thermal diffusion points, the accurate identification of quantitative size of defects are completed.

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

Disclosed herein is a method and system for automatically propagating segmentation in a medical image. In an embodiment, the method uses a segmented reference Region of Interest (RoI) in a reference image to determine segmentation parameters and a plurality of reference points. Further, method generates a plurality of translated points on a current image, in which a target RoI must be segmented, by translating the plurality of reference points onto the current image. Subsequently, relevant seeds among from the translated points are automatically selected based on the segmentation parameters. Finally, a multi-seed segmentation of the selected relevant seeds is performed for estimating and segmenting the target RoI in the current image, such that the target RoI is the propagated segmentation of the segmented RoI in the reference image.