公开(公告)号：US11842255B2

公开(公告)日：2023-12-12

申请号：US17948947

申请日：2022-09-20

Applicant: NeuroPace, Inc.

Inventor： Sharanya Arcot Desai ,  Thomas K. Tcheng ,  Benjamin E. Shanahan

IPC: G06N20/00 ,  G06N3/08 ,  G06F9/30 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2321

CPC classification number: G06N20/00 ,  G06F9/30036 ,  G06F18/2155 ,  G06F18/22 ,  G06F18/2321 ,  G06N3/08 ,  G06V2201/031

Abstract: A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.

公开(公告)号：US11823431B2

公开(公告)日：2023-11-21

申请号：US17749257

申请日：2022-05-20

Applicant: Covidien LP

Inventor： Igor A. Markov ,  Yuri Kreinin

IPC: G06K9/00 ,  G06V10/44 ,  G06T7/11 ,  G06T7/187 ,  G06T7/00 ,  G06T15/20

CPC classification number: G06V10/457 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/187 ,  G06T15/205 ,  G06T2200/04 ,  G06T2200/08 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10121 ,  G06T2207/10132 ,  G06T2207/30061 ,  G06V2201/031

Abstract: Disclosed are systems, devices, and methods for detecting a trachea, an exemplary system comprising an imaging device configured to obtain image data and a computing device configured to generate a three-dimensional (3D) model, identify a potential connected component in a first slice image, identify a potential connected component in a second slice image, label the first slice image as a top slice image, label the connected component in the top slice image as an active object, associate each connected component in a current slice image with a corresponding connected component in a previous slice image based on a connectivity criterion, label each connected component in the current slice image associated with a connected component of the preceding slice image as the active object, and identify the active object as the trachea, based on a length of the active object.

公开(公告)号：US11810334B2

公开(公告)日：2023-11-07

申请号：US16723880

申请日：2019-12-20

Applicant: Darmiyan, Inc.

Inventor： Padideh Kamali-Zare ,  Kaveh Vejdani ,  Thomas Liebmann ,  Hesaam Esfandyarpour ,  Elham Khosravi ,  Pavan Krishnamurthy

IPC: A61B5/055 ,  G01R33/56 ,  G06T7/00 ,  G16H50/50 ,  G16H50/70 ,  G16H30/40 ,  A61B5/00 ,  G01R33/563 ,  G06V10/10 ,  G06V20/69 ,  G06F18/22 ,  G06V10/75 ,  G06T17/10

CPC classification number: G06V10/10 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/4082 ,  A61B5/4088 ,  A61B5/4094 ,  A61B5/7278 ,  G01R33/5608 ,  G06F18/22 ,  G06T7/0014 ,  G06T7/0016 ,  G06T17/10 ,  G06V10/75 ,  G06V20/695 ,  G16H30/40 ,  G16H50/50 ,  G16H50/70 ,  A61B2576/026 ,  G01R33/5602 ,  G01R33/56341 ,  G06T2200/04 ,  G06T2207/10088 ,  G06T2207/30016 ,  G06V2201/031

Abstract: Methods and systems for determining whether brain tissue is indicative of a disorder, such as a neurodegenerative disorder, are provided. The methods and systems generally utilize data processing techniques to assess a level of congruence between measured parameters obtained from magnetic resonance imaging (MRI) data and simulated parameters obtained from computational modeling of brain tissues.

公开(公告)号：US11810296B2

公开(公告)日：2023-11-07

申请号：US17454163

申请日：2021-11-09

Applicant: The Regents of the University of California

Inventor： Esther L. Yuh ,  Pratik Mukherjee ,  Geoffrey T. Manley

IPC: G06K9/00 ,  G06T7/00 ,  A61B6/03 ,  A61B6/00 ,  G06T7/68 ,  G06T7/62 ,  G06V10/44 ,  G06F18/21 ,  G06F18/2431 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/82 ,  G06T7/136 ,  G06T7/11

CPC classification number: G06T7/0012 ,  A61B6/032 ,  A61B6/501 ,  G06F18/217 ,  G06F18/2431 ,  G06F18/24137 ,  G06T7/11 ,  G06T7/136 ,  G06T7/62 ,  G06T7/68 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/10081 ,  G06T2207/20061 ,  G06T2207/20072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06V2201/03 ,  G06V2201/031

Abstract: A computer-based method for quantitative evaluation of computed tomography (CT) images of the head, particularly in circumstances of neurological emergency such as acute intracranial hemorrhage, evidence of intracranial mass effect, and acute stroke. The method comprises: calculation of volumes of abnormal areas such as locations of hemorrhage; quantification of severity of midline shift and basilar cistern effacement; and rapid identification of anatomical locations of abnormal findings. The methods comprise use of heuristics, convolutional neural networks, deep learning, edge detection, and Hough transform.

公开(公告)号：US20230334665A1

公开(公告)日：2023-10-19

申请号：US18338352

申请日：2023-06-21

Applicant: FUJIFILM Corporation

Inventor： Yuta Hiasa

IPC: G06T7/00 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06V10/82 ,  G06V2201/032 ,  G06V2201/031 ,  G06T2207/30064 ,  G06T2207/20081

Abstract: A first processor of a learning device reads out a first medical use image having a disease label from a data set stored in a memory and inputs the read out first medical use image to a first learning model. The first medical use image is normalized based on a lung field region extracted by the first learning model, and a second learning model that has not been trained and detects a disease is trained by using the normalized first medical use image and the disease label. In a case in which the second learning model is trained, a value of the uncertainty of the first medical use image is calculated based on the uncertainty simultaneously estimated by the first learning model, and the first medical use image having a large value of uncertainty is excluded from learning data.

公开(公告)号：US20230316530A1

公开(公告)日：2023-10-05

申请号：US18127991

申请日：2023-03-29

Applicant: EXINI Diagnostics AB

Inventor： Jens Filip Andreas Richter ,  Kerstin Elsa Maria Johnsson ,  Erik Konrad Gjertsson ,  Aseem Undvall Anand

IPC: G06T7/11 ,  G16H50/50 ,  G16H30/20 ,  G16H50/30 ,  G16H50/20 ,  G16H30/40 ,  A61B6/03 ,  A61B6/00 ,  A61K51/04 ,  G06V20/64 ,  G06V20/69 ,  G06V30/24 ,  G06F18/214

CPC classification number: G06T7/11 ,  G16H50/50 ,  G16H30/20 ,  G16H50/30 ,  G16H50/20 ,  G16H30/40 ,  A61B6/032 ,  A61B6/037 ,  A61B6/463 ,  A61B6/466 ,  A61B6/481 ,  A61B6/505 ,  A61B6/507 ,  A61B6/5205 ,  A61B6/5241 ,  A61B6/5247 ,  A61K51/0455 ,  G06V20/64 ,  G06V20/695 ,  G06V20/698 ,  G06V30/2504 ,  G06F18/214 ,  G06V2201/031 ,  G06V2201/033

Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.

公开(公告)号：US20230267714A1

公开(公告)日：2023-08-24

申请号：US18171744

申请日：2023-02-21

Applicant: Siemens Healthcare GmbH

Inventor： Florian THAMM ,  Markus Juergens ,  Oliver Taubmann ,  Hendrik Ditt

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/764 ,  G06V10/26 ,  G06T7/68

CPC classification number: G06V10/774 ,  G06T7/0014 ,  G06V10/764 ,  G06V10/26 ,  G06T7/68 ,  G06T2207/30101 ,  G06T2207/30008 ,  G06V2201/031 ,  G06T2207/30016 ,  G06T2207/20081

Abstract: A method for generating training data for training a deep learning algorithm, comprising: receiving medical imaging data of an examination area including a first part and a second part of a symmetric organ; splitting the medical imaging data along a symmetry plane or a symmetry axis into a first dataset and a second dataset, wherein the first dataset includes the medical imaging data of the first part and the second dataset includes the medical imaging data of the second part; mirroring the second dataset along the symmetry plane or the symmetry axis; generating the training data by stacking the first dataset and the mirrored second dataset; and providing the training data.

公开(公告)号：US11730396B2

公开(公告)日：2023-08-22

申请号：US17656653

申请日：2022-03-28

Applicant: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Jianhui Cao ,  Lei Zhang ,  Yaguang Fu ,  Suming Wang ,  Longzi Yang

IPC: A61B5/107 ,  A61B5/00 ,  A61B6/00 ,  A61B6/03 ,  A61B6/04 ,  A61B5/103 ,  G06V10/25 ,  G06T7/73 ,  A61B5/01

CPC classification number: A61B5/1077 ,  A61B5/0035 ,  A61B5/0037 ,  A61B5/0046 ,  A61B5/0077 ,  A61B5/015 ,  A61B5/1036 ,  A61B6/03 ,  A61B6/032 ,  A61B6/0407 ,  A61B6/469 ,  G06T7/74 ,  G06V10/25 ,  A61B5/6888 ,  A61B5/6889 ,  A61B5/6891 ,  A61B5/6892 ,  A61B6/0492 ,  A61B6/4417 ,  A61B6/467 ,  G06T2207/10072 ,  G06T2207/20221 ,  G06T2207/30204 ,  G06V2201/03 ,  G06V2201/031

Abstract: The present disclosure relates to systems and methods for positioning a subject. The method may include generating a first image of the subject disposed on a scanning board of an imaging device. The first image may include position information of the subject. The method may further include generating a second image of the subject which includes information associated with one or more organs of the subjects. Additionally, the method may include determining the position of a ROI based on the first image and the second image. The method may further include operating the imaging device to scan a target portion of the subject.

公开(公告)号：US11727574B2

公开(公告)日：2023-08-15

申请号：US17403687

申请日：2021-08-16

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： Darin T Okuda ,  Braeden D Newton

IPC: G06T7/11 ,  G06T15/08 ,  G06T17/00 ,  G06V10/26 ,  G06F18/22 ,  G06V10/75 ,  G16H30/40 ,  A61B5/055

CPC classification number: G06T7/11 ,  G06F18/22 ,  G06T15/08 ,  G06T17/00 ,  G06V10/26 ,  G06V10/75 ,  A61B5/055 ,  G06T2207/10088 ,  G06T2207/30016 ,  G06T2207/30096 ,  G06T2210/41 ,  G06V2201/031 ,  G16H30/40

Abstract: Methods, apparatuses, systems, and implementations for creating 3-dimensional (3D) representations exhibiting geometric and surface characteristics of brain lesions are disclosed. 2D and/or 3D MM images of the brain may be acquired. Brain lesions and other abnormalities may be identified and isolated with each lesion serving as a region of interest (ROI). Saved ROI may be converted into stereolithography format, maximum intensity projection (MIP) images, and/or orthographic projection images. Data corresponding to these resulting 3D brain lesion images may be used to create 3D printed models of the isolated brain lesions using 3D printing technology. Analysis of the 3D brain lesion images and the 3D printed brain lesion models may enable a more efficient and accurate way of determining brain lesion etiologies.

公开(公告)号：US20230222771A1

公开(公告)日：2023-07-13

申请号：US18151906

申请日：2023-01-09

Applicant: MEDIAN TECHNOLOGIES

Inventor： Hubert BEAUMONT ,  Tiffany FORIEL ,  Vincent BOBIN ,  Antoine IANNESSI

IPC: G06V10/764 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/44

CPC classification number: G06V10/764 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/44 ,  G06V2201/031 ,  A61B5/055

Abstract: A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.