公开(公告)号：US20240303974A1

公开(公告)日：2024-09-12

申请号：US18665605

申请日：2024-05-16

Applicant: FUJIFILM Corporation

Inventor： Masaaki OOSAKE

IPC: G06V10/774 ,  G06V10/10 ,  G06V10/44 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06V10/16 ,  G06V10/44 ,  G06V10/82 ,  G06V2201/031

Abstract: There are provided a learning data generation apparatus and method and a learning model generation apparatus and method that can attain efficient learning. The learning data generation apparatus acquires first image data and second image data each having a region of interest, and when a positional relationship between the region of interest of the first image data and the region of interest of the second image data satisfies a predetermined condition, combines an image of a region, of the first image data, that includes the region of interest and an image of a region, of the second image data, that includes the region of interest to generate third image data. The learning model generation apparatus acquires the third image data generated by the learning data generation apparatus and trains a learning model by using the third image data to generate the learning model.

公开(公告)号：US20240303817A1

公开(公告)日：2024-09-12

申请号：US18657646

申请日：2024-05-07

Applicant: EDWARDS LIFESCIENCES CORPORATION

Inventor： Stephen Epstein ,  Wesley Paul Nicholson

IPC: G06T7/00 ,  A61B34/00 ,  A61B34/10 ,  G06T7/62 ,  G06T11/00 ,  G06V10/77 ,  G16H30/40

CPC classification number: G06T7/0016 ,  A61B34/10 ,  A61B34/25 ,  G06T7/62 ,  G06T11/00 ,  G06V10/7715 ,  G16H30/40 ,  A61B2034/104 ,  A61B2034/105 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30052 ,  G06T2210/41 ,  G06V2201/031

Abstract: Techniques relate to analyzing image data to determine an annuloplasty ring to implant for an annuloplasty procedure. For example, image data can be received that depicts a heart valve. The image data can be analyzed to identify one or more image features that represent one or more anatomical features of the heart valve. Based on the one or more image features, heart data can be generated that indicates a measurement and/or another characteristic of the heart valve. An annuloplasty ring can be determined based on the heart valve data and/or annuloplasty ring data indicating characteristics of one or more annuloplasty rings. User interface data can then be generated that indicates the annuloplasty ring.

公开(公告)号：US12086992B2

公开(公告)日：2024-09-10

申请号：US17341140

申请日：2021-06-07

Applicant: CANON KABUSHIKI KAISHA

Inventor： Fukashi Yamazaki ,  Daisuke Furukawa

IPC: G06T7/174 ,  G06F18/2431 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11 ,  G06T7/149 ,  G06T7/162 ,  G06T7/187 ,  G06T7/194 ,  G06V10/25 ,  G06V10/26 ,  G06V10/44 ,  G06V10/762 ,  G06V10/774 ,  G06V10/82 ,  G06V20/64 ,  G16H30/40

CPC classification number: G06T7/174 ,  G06F18/2431 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11 ,  G06T7/149 ,  G06T7/162 ,  G06T7/187 ,  G06T7/194 ,  G06V10/25 ,  G06V10/26 ,  G06V10/454 ,  G06V10/7635 ,  G06V10/774 ,  G06V10/82 ,  G06V20/64 ,  G16H30/40 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/20072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20116 ,  G06T2207/20161 ,  G06T2207/30056 ,  G06T2207/30084 ,  G06V2201/031

Abstract: An image processing apparatus according to the present invention includes a first classification unit configured to classify a plurality of pixels in two-dimensional image data constituting first three-dimensional image data including an object into a first class group by using a trained classifier, and a second classification unit configured to classify a plurality of pixels in second three-dimensional image data including the object into a second class group based on a result of classification by the first classification unit, the second class group including at least one class of the first class group. According to the image processing apparatus according to the present invention, a user's burden of giving pixel information can be reduced and a region can be extracted with high accuracy.

公开(公告)号：US12082906B2

公开(公告)日：2024-09-10

申请号：US17807721

申请日：2022-06-19

Applicant: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Jingyuan Lyu ,  Yu Ding ,  Qi Liu ,  Jian Xu

IPC: A61B6/00 ,  A61B5/00 ,  A61B5/055 ,  A61B6/03 ,  G06F18/214 ,  G06T7/00 ,  G06T7/136 ,  G06T7/143

CPC classification number: A61B5/0044 ,  A61B5/055 ,  A61B5/7285 ,  A61B6/032 ,  A61B6/037 ,  A61B6/541 ,  G06F18/214 ,  G06T7/0016 ,  G06T7/136 ,  G06T7/143 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06V2201/031

Abstract: A method for medical imaging may include obtaining a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The method may also include automatically determining, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

公开(公告)号：US12067716B2

公开(公告)日：2024-08-20

申请号：US17501712

申请日：2021-10-14

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：US12062426B2

公开(公告)日：2024-08-13

申请号：US17200554

申请日：2021-03-12

Applicant: EchoNous, Inc.

Inventor： Nikolaos Pagoulatos ,  Ramachandra Pailoor ,  Kevin Goodwin

IPC: G16H30/20 ,  G16H50/20 ,  G16H30/40 ,  G06K9/62 ,  A61B8/00 ,  A61B8/08 ,  G06V10/44 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/82 ,  G06F3/04842 ,  G06T7/00 ,  G06F3/04845

CPC classification number: G16H30/20 ,  A61B8/46 ,  A61B8/461 ,  A61B8/469 ,  A61B8/5215 ,  A61B8/5223 ,  A61B8/565 ,  G06F3/04842 ,  G06F18/2414 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G06F3/04845 ,  G06T2200/24 ,  G06T2207/10132 ,  G06V2201/031

Abstract: Ultrasound image recognition systems and methods, and artificial intelligence training networks for such systems and methods, are provided. An ultrasound data information system includes an ultrasound image recognition training network that is configured to receive ultrasound training images and to develop ultrasound image knowledge based on the received ultrasound training images. An ultrasound imaging device acquires ultrasound images of a patient, and the device includes an ultrasound image recognition module. The ultrasound image recognition module is configured to receive the ultrasound image knowledge, receive the acquired ultrasound images from the ultrasound imaging device, and determine, based on the ultrasound image knowledge, whether the received ultrasound images represent a clinically desirable view of an organ or whether the clinically desirable views indicate normal function or a particular pathology. The received ultrasound images are transmitted to the ultrasound image recognition training network for further training and development of updated ultrasound image knowledge.

公开(公告)号：US20240266064A1

公开(公告)日：2024-08-08

申请号：US18614586

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：US12058180B2

公开(公告)日：2024-08-06

申请号：US17351589

申请日：2021-06-18

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Joseph Soryal ,  Dylan Chamberlain Reid

IPC: H04L29/06 ,  G06F18/22 ,  G06F21/64 ,  G06N5/04 ,  G06N20/00 ,  G08B5/22 ,  H04L9/32 ,  H04L9/40 ,  H04J1/02 ,  H04L12/46

CPC classification number: H04L63/205 ,  G06F18/22 ,  G06F21/64 ,  G06N5/04 ,  G06N20/00 ,  G08B5/22 ,  H04L9/3236 ,  H04L63/0272 ,  H04L63/1416 ,  G06V2201/031 ,  H04J1/02 ,  H04L12/4641

Abstract: Augmented reality security is enabled, e.g., to prevent transmission of maliciously manipulated augmented reality data. For instance, a device can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on a defined tampering criterion, determining whether a virtual frame, of a group of virtual frames received via a communication link established between the device and augmented reality equipment, has been modified without authorization, and in response to the virtual frame being determined to have been modified, causing the augmented reality equipment to stop displaying the group of virtual frames.

公开(公告)号：US20240257967A1

公开(公告)日：2024-08-01

申请号：US18422311

申请日：2024-01-25

Applicant: iCardio.ai

Inventor： Roman A. Sandler ,  Joseph Sokol ,  Daniel Sokol ,  Damjan Postolovski ,  Aleksandar Stojmenski ,  Markos Amsalu Muche ,  Harris Lee Bergman ,  Jack Elie Gindi

IPC: G16H50/20 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

CPC classification number: G16H50/20 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60 ,  G06V2201/031

Abstract: A method for facilitating a diagnosis of pathologies using a machine learning model includes receiving a medical data from a device, analyzing the medical data using a machine learning model comprising an artificial neural network which comprises an input layer which takes medical data as inputs, a middle layer which outputs a lower dimensional abstract vector space representation for each inputs by encoding the medical data to a lower dimensional abstract vector space, and output layers which classifies the lower dimensional abstract vector space representation to outputs corresponding to assessment parameters considered in the diagnosis of the pathologies, obtaining outputs from the machine learning model for the diagnosis of pathologies based on the analyzing, generating a result based on the outputs, and storing the result and the machine learning model.

公开(公告)号：US12039721B2

公开(公告)日：2024-07-16

申请号：US17362657

申请日：2021-06-29

Applicant: Siemens Healthineers International AG

Inventor： Daren Sawkey

IPC: G06T7/12 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2411 ,  G06N20/00 ,  G06T7/00 ,  G06T7/174 ,  G06V10/44 ,  G16H30/20 ,  A61B5/055 ,  A61B6/03 ,  A61B8/08

CPC classification number: G06T7/0012 ,  G06F18/2148 ,  G06F18/22 ,  G06F18/2411 ,  G06N20/00 ,  G06T7/174 ,  G06V10/44 ,  G16H30/20 ,  A61B5/055 ,  A61B6/032 ,  A61B6/037 ,  A61B8/08 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30168 ,  G06V2201/031

Abstract: Embodiments described herein provide for receiving a second image comprising an overlay depicting an organ-at-risk (OAR) segmentations. The overlay is generated by a first machine learning model based on a first image depicting the anatomical region of a current patient. A second machine learning model receives the second image and set of third images depicting prior patient OAR segmentations on which the second machine learning model was trained. The second machine learning model classifies the second image as one of a set of class names and characterizes the extent to which the second image is similar to, or dissimilar to, images with the same class name in the set of third images. The characterization may be based on outputs of internal layers of the second machine learning model. Dimensionality reduction may be performed on the outputs of the internal layers to present the outputs in a form comprehendible by humans.