公开(公告)号：US20240087123A1

公开(公告)日：2024-03-14

申请号：US18517138

申请日：2023-11-22

Applicant: Lunit Inc.

Inventor： Minje JANG

IPC: G06T7/00 ,  G06F18/214 ,  G06F18/25 ,  G06V10/426 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G06F18/25 ,  G06V10/426 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V20/69 ,  G06T2207/20072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06V2201/03

Abstract: An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

公开(公告)号：US20240078666A1

公开(公告)日：2024-03-07

申请号：US18241809

申请日：2023-09-01

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Jiaxuan PANG ,  Fatemeh Haghighi ,  DongAo Ma ,  Nahid Ui Islam ,  Mohammad Reza Hosseinzadeh Taher ,  Jianming Liang

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/54 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/54 ,  G16H30/40 ,  G06T2207/20081 ,  G06V2201/03

Abstract: A self-supervised machine learning method and system for learning visual representations in medical images. The system receives a plurality of medical images of similar anatomy, divides each of the plurality of medical images into its own sequence of non-overlapping patches, wherein a unique portion of each medical image appears in each patch in the sequence of non-overlapping patches. The system then randomizes the sequence of non-overlapping patches for each of the plurality of medical images, and randomly distorts the unique portion of each medical image that appears in each patch in the sequence of non-overlapping patches for each of the plurality of medical images. Thereafter, the system learns, via a vision transformer network, patch-wise high-level contextual features in the plurality of medical images, and simultaneously, learns, via the vision transformer network, fine-grained features embedded in the plurality of medical images.

公开(公告)号：US20240074721A1

公开(公告)日：2024-03-07

申请号：US18242131

申请日：2023-09-05

Applicant: Jubilant Draximage Inc.

Inventor： Eric James Moulton ,  Robert A. DeKemp ,  Indranil Nandi ,  Chad Roger Ronald Nicholas Hunter

IPC: A61B6/00 ,  A61B6/03 ,  G06T5/00 ,  G06T7/00 ,  G06T11/00 ,  G06V10/25 ,  G06V10/82

CPC classification number: A61B6/5217 ,  A61B6/037 ,  A61B6/507 ,  A61B6/5258 ,  G06T5/002 ,  G06T7/0012 ,  G06T11/005 ,  G06V10/25 ,  G06V10/82 ,  G06T2200/04 ,  G06T2207/10104 ,  G06T2207/20084 ,  G06T2207/30104 ,  G06T2211/441 ,  G06V2201/03

Abstract: The present invention provides an image processing method to assess quantitative myocardial blood flow and/or myocardial flow reserve, comprising the steps of: (a) pre-processing of images comprises: (i) reconstructing dynamic cine 3D tomographic myocardial perfusion imaging (MPI) data, (ii) optionally, denoising to improve the quality of image, (iii) extracting blood input function from a region of interest (ROI) of the left ventricle blood cavity, (iv) estimating the distribution volume (DV), given by the ratio of uptake and washout rates (K1/k2) to stabilize and improve estimation of K1, k2 and total blood volume (TBV) and subsequent myocardial blood flow measures, and (v) data normalization by dividing by the maximum of the blood input function; (b) assessing the individual signals pre-processed in step (a) in order to generate K1 and TBV parametric maps using artificial neural network;



(c) post-processing of K1, k2 and TBV parametric maps; and of rest and stress myocardial blood flow to estimate myocardial flow reserve (MFR) and/or coronary flow reserve (CFR).

公开(公告)号：US11922682B2

公开(公告)日：2024-03-05

申请号：US17221146

申请日：2021-04-02

Applicant: MERATIVE US L.P.

Inventor： Mehdi Moradi ,  Chun Lok Wong

IPC: G06V10/82 ,  G06F18/24 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N5/01 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  G06T7/00 ,  G06V10/764 ,  G06V20/69 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70

CPC classification number: G06V10/82 ,  G06F18/24 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/0014 ,  G06V10/764 ,  G06V20/69 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N3/044 ,  G06N3/047 ,  G06N5/01 ,  G06N20/10 ,  G06N20/20 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06T2207/30048 ,  G06V2201/03

Abstract: Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.

公开(公告)号：US11914918B2

公开(公告)日：2024-02-27

申请号：US17163888

申请日：2021-02-01

Applicant: CANON KABUSHIKI KAISHA

Inventor： Yoshihito Machida ,  Yoshinori Hirano ,  Hideaki Miyamoto ,  Daisuke Yamada

IPC: G06F3/14 ,  G06F18/214 ,  G06F18/21 ,  G06F18/40 ,  G09G5/14 ,  G06F3/147 ,  G16H50/20 ,  G16H30/40 ,  G06V10/774 ,  G06V10/778 ,  G06V10/94

CPC classification number: G06F3/147 ,  G06F3/14 ,  G06F18/214 ,  G06F18/2178 ,  G06F18/40 ,  G06V10/774 ,  G06V10/7784 ,  G06V10/945 ,  G16H30/40 ,  G16H50/20 ,  G06V2201/03

Abstract: A medical information processing apparatus comprises an obtaining unit that obtains medical information, a learning unit that performs learning on a function of the medical information processing apparatus using the medical information, an evaluation data holding unit that holds evaluation data in which a correct answer to be obtained by executing the function is known, the evaluation data being for evaluating a learning result of the learning unit, an evaluating unit that evaluates a learning result obtained through learning, based on the evaluation data, and an accepting unit that accepts an instruction to apply a learning result of the learning unit to the function.

公开(公告)号：US20240062526A1

公开(公告)日：2024-02-22

申请号：US18384421

申请日：2023-10-27

Applicant: Lunit Inc.

Inventor： Weonsuk LEE ,  Hyeonsoo Lee ,  Gunhee Nam ,  Taesoo Kim

IPC: G06V10/774 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06V10/7715 ,  G06V10/82 ,  G06V2201/03

Abstract: Provided is a method for training a neural network and a device thereof. The method for training a neural network with three-dimensional (3D) training image data comprising a plurality of two-dimensional (2D) training image data, comprises: training a first convolutional neural network (CNN) with the plurality of 2D training image data, wherein the first convolutional neural network comprises 2D convolutional layers; and training a second convolutional neural network with the 3D training image data, wherein the second convolutional neural network comprises the 2D convolutional layers and 3D convolutional layers configured to receive an output of the 2D convolutional layers as an input.

公开(公告)号：US20240062523A1

公开(公告)日：2024-02-22

申请号：US18161186

申请日：2023-01-30

Applicant: Siemens Medical Solutions USA, Inc.

Inventor： Gengyan Zhao ,  Youngjin Yoo ,  Thomas Re ,  Eli Gibson ,  Dorin Comaniciu

IPC: G06V10/774 ,  G06T7/00 ,  G06T5/50 ,  G06T5/00 ,  G06V10/764 ,  G16H30/40 ,  G16H50/50

CPC classification number: G06V10/774 ,  G06T7/0012 ,  G06T5/50 ,  G06T5/002 ,  G06V10/764 ,  G16H30/40 ,  G16H50/50 ,  G06T2200/04 ,  G06T2207/30016 ,  G06T2207/30096 ,  G06T2207/20212 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: Systems and methods for generating synthesized medical images of a tumor are provided. A 3D mask of an anatomical structure generated from a 3D medical image and a 3D image of a plurality of concentric spheres are received. A 3D mask of a tumor is generated based on the 3D mask of the anatomical structure and the 3D image of the plurality of concentric spheres using a first 3D generator network. A 3D intensity map of the tumor is generated based on the 3D mask of the tumor and the 3D image of the plurality of concentric spheres using a second 3D generator network. A 3D synthesized medical image of the tumor is generated based on one or more 2D slices of the 3D intensity map of the tumor and one or more 2D slices of the 3D medical image using a 2D generator network. The 3D synthesized medical image of the tumor is output.

公开(公告)号：US11908580B2

公开(公告)日：2024-02-20

申请号：US17397857

申请日：2021-08-09

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Yifan Hu ,  Yefeng Zheng

IPC: G16H50/20 ,  G16H30/40 ,  G06V10/25 ,  G06V10/26 ,  G06F18/21 ,  G06F18/22 ,  G06F18/214 ,  G06F18/25 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/80 ,  G06V10/44 ,  G06N3/08

CPC classification number: G16H50/20 ,  G06F18/214 ,  G06F18/217 ,  G06F18/22 ,  G06F18/2413 ,  G06F18/253 ,  G06V10/25 ,  G06V10/26 ,  G06V10/454 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/806 ,  G16H30/40 ,  G06N3/08 ,  G06V2201/03

Abstract: A computer device obtains a plurality of medical images. The device generates a texture image based on image data of a region of interest in the medical images. The device extracts a local feature from the texture image using a first network model. The device extracts a global feature from the medical images using a second network model. The device fuses the extracted local feature and the extracted global feature to form a fused feature. The device performs image classification based on the fused feature.

公开(公告)号：US11903906B2

公开(公告)日：2024-02-20

申请号：US17114249

申请日：2020-12-07

Applicant: Becton Dickinson Rowa Germany GmbH

Inventor： Christoph Hellenbrand

IPC: A61J7/00 ,  B65D83/04 ,  H04N13/254 ,  G06F18/24 ,  G06V10/764 ,  G06T1/00 ,  G06V10/56 ,  G06V20/66 ,  G01D5/34

CPC classification number: A61J7/0084 ,  B65D83/0409 ,  G06F18/24 ,  G06T1/0007 ,  G06V10/56 ,  G06V10/764 ,  G06V20/66 ,  H04N13/254 ,  G01D5/342 ,  G06T2207/10012 ,  G06V2201/03

Abstract: A method for providing a singling device of a storage and dispensing container for drug portions is provided. A singling device is adapted to a specific drug portion based on the measurement of the drug portion. The measurement is obtained by generating at least one image of a to-be-measured drug portion by a detection device and using image analysis to process the at least one image. Drug information for the specific drug portion is determined, the drug information including at least the dimensions of the specific drug portion. Based on the determined drug information, a singling device fitting the drug portion to be singularized is identified and provided for use in the storage and dispensing container. A system for identifying a singling device is also provided.

公开(公告)号：US11901065B2

公开(公告)日：2024-02-13

申请号：US17530232

申请日：2021-11-18

Applicant: Verb Surgical Inc.

Inventor： Jagadish Venkataraman ,  Pablo E. Garcia Kilroy

IPC: G16H30/40 ,  G16H30/20 ,  G06N20/00 ,  G06V20/40 ,  G06V20/70

CPC classification number: G16H30/40 ,  G06N20/00 ,  G06V20/41 ,  G06V20/44 ,  G06V20/46 ,  G06V20/49 ,  G06V20/70 ,  G16H30/20 ,  G06V2201/03

Abstract: Embodiments described herein provide various examples of a surgical video analysis system for segmenting surgical videos of a given surgical procedure into shorter video segments and labeling/tagging these video segments with multiple categories of machine learning descriptors. In one aspect, a process for processing surgical videos recorded during performed surgeries of a surgical procedure includes the steps of: receiving a diverse set of surgical videos associated with the surgical procedure; receiving a set of predefined phases for the surgical procedure and a set of machine learning descriptors identified for each predefined phase in the set of predefined phases; for each received surgical video, segmenting the surgical video into a set of video segments based on the set of predefined phases and for each segment of the surgical video of a given predefined phase, annotating the video segment with a corresponding set of machine learning descriptors for the given predefined phase.