公开(公告)号：US12125208B2

公开(公告)日：2024-10-22

申请号：US17446732

申请日：2021-09-02

申请人： SIEMENS HEALTHINEERS AG

发明人： Zhoubing Xu ,  Sasa Grbic ,  Dominik Neumann ,  Guillaume Chabin ,  Bruce Spottiswoode ,  Fei Gao ,  Günther Platsch

IPC分类号： G06T7/11 ,  G06T7/00 ,  G06T7/187

CPC分类号： G06T7/11 ,  G06T7/0012 ,  G06T7/187 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10136 ,  G06T2207/20081 ,  G06T2207/30016 ,  G06T2207/30056 ,  G06T2207/30061 ,  G06T2207/30101

摘要： The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

公开(公告)号：US20240338867A1

公开(公告)日：2024-10-10

申请号：US18625365

申请日：2024-04-03

申请人： National University Corporation Tokai National Higher Education and Research System ,  CANON MEDICAL SYSTEMS CORPORATION

发明人： Toshiaki TAOKA ,  Yoshiki TANAKA ,  Nobuyasu ICHINOSE ,  Takaya MORI ,  Masanori OZAKI

IPC分类号： G06T11/20 ,  G06T7/00 ,  G06T7/11

CPC分类号： G06T11/206 ,  G06T7/0012 ,  G06T7/11 ,  G06T2207/10088 ,  G06T2207/20021 ,  G06T2207/20092 ,  G06T2207/30016

摘要： An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain pixel values of at least three Magnetic Resonance (MR) images of mutually-different types. The processing circuitry is configured to generate a scatter diagram in which values based on the pixel values are arranged in a region having at least three-dimensional axes and which displays a value group related to a target tissue in such a manner that a value group related to a specific target serving as a reference is always displayed in the same position, and then cause a display to display the scatter diagram.

公开(公告)号：US20240312032A1

公开(公告)日：2024-09-19

申请号：US18184573

申请日：2023-03-15

申请人： Augmedics, Inc.

发明人： John HOLST ,  Alejandro SKLAR ,  Emmanuel NUNEZ

IPC分类号： G06T7/30 ,  A61B5/00 ,  G06T7/00 ,  G16H30/20 ,  G16H30/40

CPC分类号： G06T7/30 ,  A61B5/4566 ,  G06T7/0012 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30012 ,  G06T2207/30016

摘要： Embodiments include example systems, methods, and computer-accessible mediums for analysis and visualization of data associated with anatomical images. In some embodiments the anatomical images can be spinal images used for assessment of stenosis and disc degeneration. In some embodiments, systems, devices, and methods described herein include selecting a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine, identifying one or more anatomical parts in the ROI, determining one or more parameters of the one or more anatomical parts, and assessing a severity of a spinal deformity based on the one or more parameters.

公开(公告)号：US12093833B2

公开(公告)日：2024-09-17

申请号：US17549258

申请日：2021-12-13

申请人： Shenzhen Institutes of Advanced Technology

发明人： Shuqiang Wang ,  Wen Yu ,  Chenchen Xiao ,  Shengye Hu

IPC分类号： G06V10/32 ,  G06N3/088 ,  G06T7/00 ,  G06V10/26 ,  G06V10/34 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

CPC分类号： G06N3/088 ,  G06T7/0012 ,  G06V10/267 ,  G06V10/32 ,  G06V10/34 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016

摘要： A visualization method for evaluating brain addiction traits, an apparatus, and a computer-readable storage medium are provided. The method includes the following. A visualization processing request is received from a client, where the visual processing request contains an image to-be-processed. The image to-be-processed is masked to obtain a perturbation image masked. The perturbation image is classified with a visualization processing model to obtain a classification result, and the classification result is calculated to obtain an evaluation value of the perturbation image, where the evaluation value of the perturbation image is less than an evaluation value of the image to-be-processed without masking. The visualization evaluation result is determined according to the evaluation value of the perturbation image. The visualization evaluation result is sent to the client.

公开(公告)号：US12080426B2

公开(公告)日：2024-09-03

申请号：US17217082

申请日：2021-03-30

申请人： University of Florida Research Foundation, Inc.

发明人： Qing Lu ,  Shan Zhang ,  Tingting Hou

IPC分类号： G16B20/00 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00 ,  G16B5/20 ,  G16H10/60 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30

CPC分类号： G16H50/20 ,  G06N3/04 ,  G06N3/08 ,  G06T7/0012 ,  G16B5/20 ,  G16H10/60 ,  G16H30/40 ,  G16H50/30 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016

摘要： Various examples of methods and systems are provided related to functional deep neural networks (FDNNs), which can be used for high dimensional data analysis. In one example, a FDNN can be trained with a training set of omic data to produce a trained FDNN model. The likelihood of a condition can be determined based upon output indications of the FDNN corresponding to the one or more phenotypes, with the output indications based upon analysis of omic data including a multi-level omic profile from an individual by the trained FDNN. The FDNN model can include a series of basis functions as layers to capture complexity between the omic data with disease phenotypes. A treatment or prevention strategy for the individual can be identified based at least in part upon the likelihood of the condition.

公开(公告)号：US12080052B2

公开(公告)日：2024-09-03

申请号：US17749775

申请日：2022-05-20

申请人： Mayo Foundation for Medical Education and Research ,  Arizona Board of Regents on Behalf of Arizona State University

发明人： Leland S. Hu ,  Kristin R. Swanson ,  J. Ross Mitchell ,  Nhan L. Tran ,  Jing Li ,  Teresa Wu

IPC分类号： G06K9/00 ,  G06T7/00 ,  G06T7/11 ,  G06V10/776

CPC分类号： G06V10/776 ,  G06T7/0016 ,  G06T7/11 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30096

摘要： Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

公开(公告)号：US12079960B2

公开(公告)日：2024-09-03

申请号：US17693166

申请日：2022-03-11

申请人： TERRAN BIOSCIENCES INC. ,  THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK ,  THE RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC.

发明人： Samuel Clark ,  Kenneth Wengler ,  Guillermo Horga Hernandez

IPC分类号： G06T5/50 ,  A61B5/055 ,  G06T5/90 ,  G06T7/00 ,  G16H50/20

CPC分类号： G06T5/50 ,  A61B5/055 ,  G06T5/90 ,  G06T7/0014 ,  G16H50/20 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30016

摘要： An exemplary system, method and computer-accessible medium for harmonizing neuromelanin (NM) data using combat directly on a NM database or using combat generated coefficients to harmonize future data can include, for example, receiving imaging information of a brain of the patient(s), from one MRI scanner, receiving imaging information of a brain of the patient(s), from a second MRI scanner and using combat to harmonize the data between scanners against a reference dataset. The Neuromelanin (NM) concentration of the patient(s) can then be determined based on the harmonized data. The NM concentration can be determined using a voxel-wise analysis procedure. The voxel-wise analysis procedure can be used to determine a topographical pattern(s) within a substantia nigra (SN) of the brain of the patient(s).

公开(公告)号：US20240273714A1

公开(公告)日：2024-08-15

申请号：US18412271

申请日：2024-01-12

申请人： FUJIFILM Healthcare Corporation

发明人： Suguru Yokosawa ,  Atsuro Suzuki ,  Toru Shirai

IPC分类号： G06T7/00 ,  G06T5/50 ,  G06T5/70 ,  G06V10/25

CPC分类号： G06T7/0012 ,  G06T5/50 ,  G06T5/70 ,  G06V10/25 ,  G06T2207/20104 ,  G06T2207/20221 ,  G06T2207/20224 ,  G06T2207/30016

摘要： Provided is a noise-reduced image in which noise and artifacts, which pose a problem in intraoperative MRI, are reduced for each pixel in a manner desired by a user and a preference of the user for a tissue or a site that the user wants to observe is reflected. In a case of generating and presenting a third medical image by using a first medical image acquired by an MRI apparatus and a second medical image obtained by performing processing of reducing noise and artifacts with respect to the first medical image, a difference for each pixel between the first medical image and the second medical image is taken, a weighting value for each pixel is calculated using a generated difference image, and a user's change for weighting is received. The finally decided-on weighting value is used to combine the first medical image and the second medical image through weighted averaging for each pixel, and the combined image is presented.

公开(公告)号：US12062445B2

公开(公告)日：2024-08-13

申请号：US16837524

申请日：2020-04-01

申请人： OPTOS PLC

发明人： Alan Duncan Fleming

IPC分类号： G16H50/20 ,  A61B5/00 ,  G06T7/00

CPC分类号： G16H50/20 ,  A61B5/7275 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20182 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06T2207/30096

摘要： A computer-implemented method, system, and computer-readable medium, for determining a pathologic condition from a medical image of a portion of a subject. The method includes acquiring a plurality of lesion locations in the medical image; applying a clustering algorithm to the plurality of lesion locations to identify at least one lesion cluster and corresponding lesion cluster data; categorizing each lesion cluster into one of a set of predetermined categories based on the identified lesion cluster data; applying at least one function to the lesion cluster data with regard to each category of the set of predetermined categories, wherein the at least one function provides a fixed number of data outputs; and determining a pathologic condition by processing the fixed number of data outputs of each category of the set based on a classification algorithm trained on image data defining medical images of the portion of a plurality of subjects.

公开(公告)号：US20240257500A1

公开(公告)日：2024-08-01

申请号：US18605307

申请日：2024-03-14

申请人： THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

发明人： Lawrence R. Frank ,  Vitaly L. Galinsky

IPC分类号： G06V10/77 ,  A61B5/00 ,  A61B5/055 ,  G01R33/48 ,  G01R33/56 ,  G01R33/563 ,  G06F18/2135 ,  G06F18/2413 ,  G06T7/00

CPC分类号： G06V10/7715 ,  A61B5/00 ,  A61B5/055 ,  G01R33/5608 ,  G06F18/2135 ,  G06F18/24133 ,  G06T7/0012 ,  G01R33/4806 ,  G01R33/56341 ,  G06F2218/12 ,  G06T7/0016 ,  G06T2207/10044 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20048 ,  G06T2207/30016

摘要： Analysis of complex spatio-temporal data within a dynamic system that includes spatial positions and fields, at least a portion of which are interacting, includes determining values of mean field at every spatial position, determining spatio-temporal eigenmodes in spatial-frequency space assuming interacting fields, and determining spatial and temporal interactions between the eigenmodes. The resulting display indicates space/time localization patterns that are indicative of connectivity within the dynamic system.