公开(公告)号：US20240159849A1

公开(公告)日：2024-05-16

申请号：US18387862

申请日：2023-11-08

Applicant: SHANXI UNIVERSITY

Inventor： Yuhui DU

IPC: G01R33/56 ,  G06V10/32 ,  G06V10/77

CPC classification number: G01R33/5608 ,  G06V10/32 ,  G06V10/7715 ,  G06V2201/031

Abstract: A group information guided smooth independent component analysis method for brain functional network analysis is provided. The method includes: performing independent component analysis on multi-subject fMRI data to obtain independent components at the group level; constructing multi-objective function that reflects independence of component of individual subject, correspondence of component across different subjects, and spatial smoothness of component based on iterative reference component that are initialized using group-level independent component, iterative voxel-level features that are computed based on reference component, and individual subject's fMRI data; iteratively optimizing multi-objective function to estimate independent components; and obtaining brain functional networks and calculating time courses of brain functional networks for individual subject.

公开(公告)号：US20240148235A1

公开(公告)日：2024-05-09

申请号：US18402767

申请日：2024-01-03

Applicant: FUJIFILM Corporation

Inventor： Yuma HORI ,  Yuya Kimura ,  Eiichi Imamichi

IPC: A61B1/00 ,  A61B1/31 ,  A61B34/00 ,  A61B34/20 ,  G06V20/50 ,  G16H15/00 ,  G16H40/63

CPC classification number: A61B1/000094 ,  A61B1/00006 ,  A61B1/0005 ,  A61B1/31 ,  A61B34/25 ,  G06V20/50 ,  G16H15/00 ,  G16H40/63 ,  A61B2034/2065 ,  G06V2201/031 ,  G06V2201/034

Abstract: There are provided an information processing apparatus, an information processing method, an endoscope system, and a report creation support device which can efficiently input information on a site. The information processing apparatus includes a first processor. The first processor acquires an image captured using an endoscope, displays the acquired image in a first region on a screen of a first display unit, detects a specific region in a hollow organ from the acquired image, displays a plurality of sites constituting the hollow organ to which the detected specific region belongs, in a second region on the screen of the first display unit, and accepts selection of one site from among the plurality of sites.

公开(公告)号：US20240127613A1

公开(公告)日：2024-04-18

申请号：US18395710

申请日：2023-12-25

Applicant: FUJIFILM Corporation

Inventor： Yuta HIASA

IPC: G06V20/70 ,  G06T7/00 ,  G06T7/33 ,  G06V10/25 ,  G06V10/774 ,  G06V10/776

CPC classification number: G06V20/70 ,  G06T7/0012 ,  G06T7/344 ,  G06V10/25 ,  G06V10/774 ,  G06V10/776 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/30064 ,  G06V10/764 ,  G06V2201/031

Abstract: Provided are a disease label creation device, a disease label creation method, a disease label creation program, a learning device, and a disease detection model that can create a disease label for a simple X-ray image at a low annotation cost. An information acquisition unit of a first processor of a disease label creation device acquires a simple X-ray image, a three-dimensional CT image paired with the simple X-ray image, and a three-dimensional first disease label extracted from the CT image. A registration processing unit of the first processor performs registration between the simple X-ray image and the CT image. A disease label converter of the first processor converts the first disease label into a two-dimensional second disease label corresponding to the simple X-ray image on the basis of a result of the registration by the registration processing unit to create a disease label.

公开(公告)号：US11951331B2

公开(公告)日：2024-04-09

申请号：US17605394

申请日：2020-04-24

Applicant: Memorial Sloan Kettering Cancer Center

Inventor： Jose Ricardo Otazo Torres ,  Li Feng

IPC: A61N5/10 ,  G01R33/48 ,  G01R33/56 ,  G06T7/246 ,  G06V20/64 ,  G06V40/20

CPC classification number: A61N5/1049 ,  A61N5/1067 ,  G01R33/4826 ,  G01R33/5608 ,  G06T7/248 ,  G06V20/64 ,  G06V40/20 ,  A61N2005/1055 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30241 ,  G06V2201/031

Abstract: Described is an approach for tracking 3D organ motion in real-time using magnetic resonance imaging (MRI). The approach may include offline learning, which may acquire signature and 3D imaging data over multiple respiratory cycles to create a database of high-resolution 3D motion states. The approach may further include online matching, which may acquire signature data only in real-time (latency less than 0.2 seconds). From a motion state and motion signature database, the 3D motion state whose signature best (or sufficiently) matches the newly-acquired signature data may be selected. Real-time 3D motion tracking may be accomplished by performing time-consuming acquisition and reconstruction work in an offline learning phase, leaving just signature acquisition and correlation analysis in an online matching step, minimizing or otherwise reducing latency. The approach may be used to adapt radiotherapy procedures based on tumor motion using a magnetic resonance linear accelerator (MR-Linac) system.

公开(公告)号：US11948389B2

公开(公告)日：2024-04-02

申请号：US17693272

申请日：2022-03-11

Applicant: Kanchan Ghimire ,  Quan Chen ,  Xue Feng

Inventor： Kanchan Ghimire ,  Quan Chen ,  Xue Feng

IPC: G06V40/10 ,  G06V10/26 ,  G06V10/28 ,  G06V10/34 ,  G06V10/44 ,  G16H30/20 ,  G16H30/40

CPC classification number: G06V40/10 ,  G06V10/267 ,  G06V10/273 ,  G06V10/28 ,  G06V10/34 ,  G06V10/457 ,  G16H30/20 ,  G16H30/40 ,  G06V2201/031

Abstract: The present disclosure relates to a method and apparatus for automatic detection of anatomical sites from tomographic images. The method includes: receiving 3D images obtained by a CT or an MRI system, transforming the images to the DICOM standard patient-based coordinate system, pre-processing the images to have normalized intensity values based on their modality, performing body segmentation, cropping the images to remove excess areas outside the body, and detecting different anatomical sites including head and neck, thorax, abdomen, male pelvis and female pelvis, wherein the step of detecting different anatomical sites comprises: performing slice-level analyses on 2D axial slices to detect the head and neck region using dimensional measurement thresholds based on human anatomy, calculating lung ratios on axial slices to find if lungs are present, determining whether 3D images with lungs present span over the thoracic region, abdomen region, or both, conducting 2D connectivity analyses on axial slices to detect the pelvis region if two separate leg regions are found and differentiating detected pelvis regions as either male pelvis or female pelvis regions based on human anatomy.

公开(公告)号：US11935283B2

公开(公告)日：2024-03-19

申请号：US17299685

申请日：2019-11-14

Applicant: Union Strong (Beijing) Technology Co. Ltd.

Inventor： Hailan Jin ,  Ling Song ,  Yin Yin ,  Guangming Yang ,  Yangyang Yao ,  Pengxiang Li ,  Lan Qin

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06V10/82 ,  G06T7/0014 ,  G06V10/764 ,  G06T2207/10081 ,  G06T2207/30016 ,  G06T2207/30168 ,  G06V2201/031 ,  G06V2201/033

Abstract: Disclosed are a cranial CT-based grading method and a corresponding system, which relate to the field of medical imaging. The cranial CT-based grading method as disclosed solves the problems of relatively great subjective disparities and poor operability in eye-balling ASPECTS assessment. The grading method includes: determining frames where target image slices are located from to-be-processed multi-frame cranial CT data; extracting target areas; performing infarct judgment on each target area included in the target areas to output an infarct judgment outcome regarding the target area; and outputting a grading outcome based on infarct judgment outcomes regarding all target areas. The grading method and system as disclosed may eliminate or mitigate the diagnosis disparities caused by human factors and imaging deviations due to different imaging devices, and shorten the time taken by human observation, consideration, and bared-eye grading, thereby serving as a computer-aided method to provide reference for medical studies on stoke.

公开(公告)号：US11918438B2

公开(公告)日：2024-03-05

申请号：US17533163

申请日：2021-11-23

Applicant: 3SHAPE A/S

Inventor： Henrik John Brandt

IPC: G06K9/00 ,  A61C9/00 ,  G06T17/00 ,  G16H30/20 ,  G16H50/50

CPC classification number: A61C9/0053 ,  G06T17/00 ,  G16H30/20 ,  G16H50/50 ,  G06V2201/031 ,  G06V2201/12

Abstract: Disclosed is method, a user interface, a computer program product and a system for predicting future development of a dental condition of a patient's set of teeth, wherein the method comprises:—obtaining two or more digital 3D representations for the teeth recorded at different points in time;—deriving based on the obtained digital 3D representations a formula expressing the development of the dental condition in terms of at least one parameter as a function of time; and—predicting the future development of the condition based on the derived formula.

公开(公告)号：US11915464B2

公开(公告)日：2024-02-27

申请号：US16464167

申请日：2017-11-23

Applicant: CONTEXTFLOW GMBH

Inventor： Rene Donner

IPC: G06V10/44 ,  G16H30/20 ,  G16H30/40 ,  G06N3/08 ,  G06F16/51 ,  G06F16/58 ,  G06F16/583 ,  G06V10/82

CPC classification number: G06V10/454 ,  G06F16/51 ,  G06F16/583 ,  G06F16/5866 ,  G06N3/08 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G06V2201/031

Abstract: Embodiments of the disclosure are directed to methods and systems for creating a medical image database, wherein data which comprise partial images of two-dimensional or higher-dimensional initial images of parts of the human body are created, a projection for obtaining feature vectors is created from the partial images, wherein, in order to prepare the execution of the projection, a neural network based on specified learning partial images is created, wherein the data records are used within the scope of a metric learning method to learn the projection and creation of the feature vectors from learning partial images. This is achieved for example by specifying learning partial images that are slightly shifted, rotated, skewed or stretched relative to one another and were created starting from the same initial image as similar.

公开(公告)号：US20240062118A1

公开(公告)日：2024-02-22

申请号：US18499909

申请日：2023-11-01

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 ,  G06N3/08 ,  G06F9/30036 ,  G06F18/2155 ,  G06F18/22 ,  G06F18/2321 ,  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：US11908136B2

公开(公告)日：2024-02-20

申请号：US17935619

申请日：2022-09-27

Applicant: Taichung Veterans General Hospital ,  Tunghai University

Inventor： Ming-Cheng Chan ,  Kai-Chih Pai ,  Wen-Cheng Chao ,  Yu-Jen Huang ,  Chieh-Liang Wu ,  Min-Shian Wang ,  Chien-Lun Liao ,  Ta-Chun Hung ,  Yan-Nan Lin ,  Hui-Chiao Yang ,  Ruey-Kai Sheu ,  Lun-Chi Chen

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/764 ,  G06T7/174

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/174 ,  G06V10/764 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30061 ,  G06V2201/031

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes an original physiological parameter inputting step, an original chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the original chest images, segmenting images of a left lung, a right lung and a heart from each of the original chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training two respiratory status classifiers using a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.