Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.

Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for image segmentation are provided. The systems may obtain a target image and a template image relating to the target image. The template image may correspond to an initial mask reflecting initial segmentations of the template image. The systems may determine a first transformation and an intermediate template image by preliminarily registering the template image to the target image and generate an intermediate mask based on the initial mask and the first transformation. The systems may determine, based on the intermediate mask, one or more first regions from the target image and one or more second regions from the intermediate template image. The systems may determine a second transformation by registering each of the one or more second regions to a corresponding first region. The systems may determine a target mask according to which the target image can be segmented based on one or more second transformations.

Methods and apparatus for predicting performance of an individual on a task, the method comprises receiving brain imaging data for the individual, wherein the brain imaging data comprises structural brain data, determining values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels, and predicting based on the determined values, a performance potential of the individual.

Embodiments provide a deep learning framework to accurately segment liver and spleen using a convolutional neural network with both short and long residual connections to extract their radiomic and deep features from multiparametric MRI. Embodiments will provide an “ensemble” deep learning model to quantify biopsy derived liver fibrosis stage and percentage using the integration of multiparametric MRI radiomic and deep features, MRE data, as well as routinely available clinical data. Embodiments will provide a deep learning model to quantify MRE-derived liver stiffness using multiparametric MRI, radiomic and deep features and routinely-available clinical data.

The present invention is in the field of methods for determining material properties from foam samples. It relates to a computer-implemented method for determining a material property of a foam sample comprising (a) providing a representation of the sample, (b) extracting at least one structural feature from the representation, wherein the at least one structural feature comprises walls, struts, or nodes (c) providing the at least one structural feature to a material model suitable for obtaining at least one material property from the structural feature, and (d) outputting the at least one material property received from the material model.