公开(公告)号：US11693919B2

公开(公告)日：2023-07-04

申请号：US16908148

申请日：2020-06-22

Applicant: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.

Inventor： Xiao Chen ,  Pingjun Chen ,  Zhang Chen ,  Terrence Chen ,  Shanhui Sun

IPC: G06F18/213 ,  G16H50/50 ,  G16H30/20 ,  G16H50/70 ,  G16H50/20 ,  G16H30/40 ,  G06V40/20 ,  G06N3/08 ,  G06F18/214

CPC classification number: G06F18/213 ,  G06F18/2148 ,  G06N3/08 ,  G06V40/20 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G16H50/70 ,  G06V2201/031

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.

公开(公告)号：US20230186603A1

公开(公告)日：2023-06-15

申请号：US18065718

申请日：2022-12-14

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Fuyue WANG ,  Yanhua WANG ,  Qilin XIAO

IPC: G06V10/764 ,  G06V10/26 ,  G06V10/82 ,  G06V10/77

CPC classification number: G06V10/764 ,  G06V10/267 ,  G06V10/82 ,  G06V10/7715 ,  G06V2201/031

Abstract: A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry receives a medical image of a target region. The processing circuitry generates an image pair including a local image having local features of the target region and a global image having global features of the target region on the basis of the received medical image. The processing circuitry performs segmentation and classification of the target region on the image pair by a neural network.

公开(公告)号：US20230172575A1

公开(公告)日：2023-06-08

申请号：US17996833

申请日：2021-04-21

Applicant: NARODOWY INSTYTUT KARDIOLOGII STEFANA KARDYNALA WYSZYNSKIEGO - PANSTWOWY INSTYTUT BADAWCZY

Inventor： Cezary KEPKA ,  Mariusz KRUK ,  Anna OLEKSIAK

IPC: A61B6/00 ,  G06V10/26 ,  G06V10/25 ,  G06T11/00 ,  G16H30/40

CPC classification number: A61B6/5217 ,  A61B6/503 ,  A61B6/504 ,  A61B6/507 ,  G06V10/26 ,  G06V10/25 ,  G06T11/005 ,  G16H30/40 ,  G06T2211/404 ,  G06V2201/031

Abstract: The invention relates to a method for identifying an ischaemic region (On) of an organ based on anatomical data, wherein the ischaemic region (On) is 0.2 to 1 part of the stenosed region at risk (Oz) downstream of the threshold point (Pprog). The size of the ischaemic region (On) is proportional to the difference between the indicative value at the threshold point (Pprog) and at the measuring point (Ppom) in the artery. The invention also relates to a system for identifying organ ischaemia, a computer program for identifying organ ischaemia and a computer program product.

公开(公告)号：US11657508B2

公开(公告)日：2023-05-23

申请号：US16734599

申请日：2020-01-06

Applicant: EXINI Diagnostics AB

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

IPC: G06T7/00 ,  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 ,  A61B6/032 ,  A61B6/037 ,  A61B6/463 ,  A61B6/466 ,  A61B6/481 ,  A61B6/505 ,  A61B6/507 ,  A61B6/5205 ,  A61B6/5241 ,  A61B6/5247 ,  A61K51/0455 ,  G06F18/214 ,  G06V20/64 ,  G06V20/695 ,  G06V20/698 ,  G06V30/2504 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50 ,  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：US11648063B2

公开(公告)日：2023-05-16

申请号：US17113385

申请日：2020-12-07

Applicant: Advanced Neuromodulation Systems, Inc.

Inventor： Yagna Pathak ,  Hyun-Joo Park ,  Simeng Zhang ,  Anahita Kyani ,  Erika Ross ,  Dehan Zhu ,  Douglas Lautner

IPC: A61B8/00 ,  A61B34/20 ,  G06K9/62 ,  G06T7/73 ,  A61N1/05 ,  G16H30/40 ,  G16H20/40 ,  G16H50/20 ,  G06V10/40 ,  A61B90/00

CPC classification number: A61B34/20 ,  A61N1/0534 ,  G06K9/6257 ,  G06T7/73 ,  G06V10/40 ,  G16H20/40 ,  G16H30/40 ,  G16H50/20 ,  A61B2034/2065 ,  A61B2090/374 ,  A61B2090/3762 ,  G06T2207/10064 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06V2201/031

Abstract: The present disclosure provides systems and methods for estimating an orientation of an implanted deep brain stimulation (DBS) lead. Such methods include generating an initial image dataset, down-sampling a respective image or adding noise to images of the subset of the initial image dataset, and re-slicing at least a subset of the modified image dataset along an alternative primary imaging axis, to generate an integrated image dataset. The method also include partitioning the integrated image dataset into a preliminary training image dataset and a testing image dataset, and re-sizing at least a subset of the preliminary training image dataset with a localized field of view around a depicted DBS lead, to generate a training image dataset. The method further includes training a machine-learning model using the training image dataset, and executing the trained machine-learning model to estimate, during a DBS implantation procedure, an orientation of a subject implanted DBS lead.

公开(公告)号：US20240362784A1

公开(公告)日：2024-10-31

申请号：US18771363

申请日：2024-07-12

Applicant: GENENTECH, INC.

Inventor： Fang-Yao HU

IPC: G06T7/00 ,  A61B5/00 ,  G06N3/08 ,  G06V10/25 ,  G06V10/94 ,  G06V20/69 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70

CPC classification number: G06T7/0012 ,  A61B5/4325 ,  A61B5/4845 ,  A61B5/4848 ,  G06N3/08 ,  G06V10/25 ,  G06V10/95 ,  G06V20/693 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  G06T2207/30024 ,  G06T2207/30242 ,  G06V2201/031

Abstract: The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.

公开(公告)号：US20240355098A1

公开(公告)日：2024-10-24

申请号：US18626419

申请日：2024-04-04

Applicant: The University Court of the University Of Edinburgh ,  CANON MEDICAL SYSTEMS CORPORATION

Inventor： Xiao LIU ,  Sotirios A. TSAFTARIS ,  Alison Q. O'NEIL ,  Pedro SANCHEZ

IPC: G06V10/774 ,  G06V10/26

CPC classification number: G06V10/774 ,  G06V10/26 ,  G06V2201/031 ,  G06V2201/034

Abstract: A medical image processing apparatus includes a memory storing training medical images, each annotated with respective weak supervision annotation information relating to at least one object represented in the training medical image, the at least one object comprising an anatomical object, a pathology, or a medical device; and processing circuitry configured to use the plurality of training medical images to train a deep learning network to perform a task, wherein the training of the deep learning network includes training a compositional latent representation comprising a plurality of kernels, wherein the training of the compositional latent network includes using the weak supervision annotation information to provide weak supervision of the training of the computational latent representation, thereby guiding the compositional latent representation towards a representation in which different ones of the kernels are representative of different objects, the different objects comprising at least one anatomical object, pathology, or medical device.

公开(公告)号：US20240303927A1

公开(公告)日：2024-09-12

申请号：US18279196

申请日：2022-02-25

Applicant: Covidien LP

Inventor： Ariel Birenbaum ,  Ofer Barasofsky ,  Guy Alexandroni ,  Irina Shevlev

IPC: G06T17/20 ,  A61B34/10 ,  G06V10/26 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/86 ,  G06V20/50 ,  G16H50/50

CPC classification number: G06T17/20 ,  A61B34/10 ,  G06V10/26 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/86 ,  G06V20/50 ,  G16H50/50 ,  A61B2034/105 ,  G06T2200/04 ,  G06T2200/24 ,  G06T2210/41 ,  G06V10/774 ,  G06V2201/031

Abstract: Systems and methods of image processing include a processor in communication with a display and a computer readable recording medium having instructions executed by the processor to read a three-dimensional (3D) image data set from the computer-readable recording medium and automatically generate a tree structure of blood vessels based on patient images of the image data set using a neural network. Manually- and/or semi-automatically-generated 3D models of blood vessels are used to train the neural network. The systems and methods involve segmenting and classifying blood vessels in the 3D image data set using the trained neural network, closing holes, finding roots and endpoints in the segmentation, finding shortest paths between the roots and endpoints, selecting most probable paths, combining most probable paths into directed graphs, solving overlaps between directed graphs, and creating 3D models of blood vessels based on the directed graphs.

公开(公告)号：US20240266067A1

公开(公告)日：2024-08-08

申请号：US18614597

申请日：2024-03-22

Applicant: Cleerly, Inc.

Inventor： James K. Min ,  James P. Earls ,  Shant Malkasian ,  Hugo Miguel Rodrigues Marques ,  Chung Chan ,  Shai Ronen

IPC: G16H50/30 ,  A61B5/00 ,  A61B5/02 ,  G06T7/00 ,  G06T7/10 ,  G06T7/60 ,  G06V20/50 ,  G16H30/40

CPC classification number: G16H50/30 ,  A61B5/02028 ,  A61B5/4848 ,  G06T7/0016 ,  G06T7/10 ,  G06T7/60 ,  G06V20/50 ,  G16H30/40 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10101 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06V2201/031

Abstract: Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

公开(公告)号：US20240266065A1

公开(公告)日：2024-08-08

申请号：US18614592

申请日：2024-03-22

Applicant: Cleerly, Inc.

Inventor： James K. Min ,  James P. Earls ,  Shant Malkasian ,  Hugo Miguel Rodrigues Marques ,  Chung Chan ,  Shai Ronen

IPC: G16H50/30 ,  A61B5/00 ,  A61B5/02 ,  G06T7/00 ,  G06T7/10 ,  G06T7/60 ,  G06V20/50 ,  G16H30/40

CPC classification number: G16H50/30 ,  A61B5/02028 ,  A61B5/4848 ,  G06T7/0016 ,  G06T7/10 ,  G06T7/60 ,  G06V20/50 ,  G16H30/40 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10101 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06V2201/031

Abstract: Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.