公开(公告)号：US20240005492A1

公开(公告)日：2024-01-04

申请号：US18247329

申请日：2021-09-21

Applicant: Siemens Healthcare GmbH ,  Herlev and Gentofte Hospital

Inventor： Oliver TAUBMANN ,  Eva EIBENBERGER ,  Michael SUEHLING ,  Christoph Felix MUELLER ,  Mathias Willadsen BREJNEBOEL

IPC: G06T7/00 ,  G16H30/40 ,  G06T7/11 ,  G06V20/70 ,  G06V20/50 ,  G06V10/774 ,  G06V10/82 ,  A61B90/00

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G06T7/11 ,  G06V20/70 ,  G06V20/50 ,  G06V10/774 ,  G06V10/82 ,  A61B90/36 ,  G06T2207/20084 ,  G06T2200/04 ,  G06T2207/20081 ,  G06T2207/30204 ,  G06T2207/30004 ,  G06T2207/10081 ,  G06V2201/031

Abstract: In particular, one or more example embodiments relates to a (e.g. computer-implemented) method for detecting free intra-abdominal air. The method comprises—receiving input data, said input data comprising a medical imaging data set of an abdominal region of a patient, e.g. via a first interface; applying a trained function, wherein the output data is generated, providing the output data e.g. via a second interface.

公开(公告)号：US11844636B2

公开(公告)日：2023-12-19

申请号：US17735792

申请日：2022-05-03

Applicant: The Board of Trustees of the Leland Stanford Junior University

Inventor： Greg Zaharchuk ,  John M. Pauly ,  Enhao Gong

IPC: G06K9/00 ,  A61B6/03 ,  G16H20/40 ,  G16H50/50 ,  G16H30/40 ,  G06N20/10 ,  G06T3/40 ,  G06T7/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82

CPC classification number: A61B6/037 ,  G06N20/10 ,  G06T3/4007 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G16H20/40 ,  G16H30/40 ,  G16H50/50 ,  G06T2207/10081 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/20004 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06V2201/031

Abstract: A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

公开(公告)号：US20230389879A1

公开(公告)日：2023-12-07

申请号：US17805366

申请日：2022-06-03

Applicant: Optum, Inc.

Inventor： David Alexander Dickie

IPC: A61B5/00 ,  G06T7/00 ,  G06T7/11 ,  G06V10/762 ,  A61B5/055

CPC classification number: A61B5/7275 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/762 ,  A61B5/055 ,  A61B5/0042 ,  G06T2207/10088 ,  G06T2207/30016 ,  G06T2207/20081 ,  G06V2201/031 ,  G06T2200/04 ,  A61B2576/026

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive recommendations using an MRI acquisition set associated with a common target object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive recommendations based at least in part on an MRI set and utilizing one or more of techniques using image preprocessing models, techniques using image segmentation models, techniques using voxel integrity score generation machine learning models, and techniques using integrity score normalization models.

公开(公告)号：US20230377149A1

公开(公告)日：2023-11-23

申请号：US18366628

申请日：2023-08-07

Applicant: FUJIFILM Corporation

Inventor： Yuta HIASA

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/70 ,  G06V10/25 ,  G06V20/70 ,  G06V10/774 ,  G06V10/776

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/70 ,  G06V10/25 ,  G06V20/70 ,  G06V10/774 ,  G06V10/776 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06T2207/10116 ,  G06V2201/032 ,  G06V2201/031 ,  G06T2207/30101 ,  G06T2207/30061 ,  G06T2207/30048

Abstract: Provided are a learning apparatus, a learning method, a trained model, and a program capable of efficiently performing learning for disease detection with high accuracy while suppressing a cost. A learning apparatus (100) includes a processor (129), a memory (114) that stores a data set of a medical image and lesion information included in the medical image, and a learning model (126) with an attention mechanism (128) that estimates a disease from an input medical image. The processor performs processing of specifying a position of a region of interest indicated by an attention map (208) in organ labeling information (206), and outputting a specification result (210), processing of calculating an error by comparing an estimation result (212) with lesion information (204), processing of setting the error on the basis of the specification result (210), and processing of causing the learning model (126) to perform learning by using the set error.

公开(公告)号：US20230368880A1

公开(公告)日：2023-11-16

申请号：US18357143

申请日：2023-07-23

Applicant: FUJIFILM Corporation

Inventor： Yuta HIASA

IPC: G16H15/00 ,  G06T7/00 ,  G06V10/774

CPC classification number: G16H15/00 ,  G06T7/0012 ,  G06V10/774 ,  G06T2207/20081 ,  G06T2207/10081 ,  G06V2201/031 ,  G06V2201/12

Abstract: The learning apparatus includes a processor (129), a memory (114), and a learning model (126). The processor (129) performs processing of inputting a pseudo simple X-ray image (204), which is generated by projecting an X-ray CT image (202), to the learning model (126), processing of generating a second interpretation report (208) with respect to the pseudo simple X-ray image (204) by converting a first interpretation report (206), processing of acquiring an error between an estimation report (210) with respect to the pseudo simple X-ray image (204) output by the learning model (126) on the basis of the input pseudo simple X-ray image (204), and the second interpretation report (208), and processing of training the learning model (126) by using the error.

公开(公告)号：US11816835B2

公开(公告)日：2023-11-14

申请号：US17353105

申请日：2021-06-21

Applicant: James R. Glidewell Dental Ceramics, Inc.

Inventor： Grant Bullis ,  Tao Nguyen ,  Greg Minzenmayer

IPC: G06K9/00 ,  G06T7/00 ,  A61C13/00 ,  A61B5/055 ,  A61B6/14 ,  A61C8/00 ,  A61C9/00 ,  G16H20/40

CPC classification number: G06T7/0012 ,  A61B5/055 ,  A61B6/14 ,  A61C8/0001 ,  A61C9/004 ,  A61C13/0004 ,  G06T2207/30004 ,  G06V2201/031 ,  G16H20/40

Abstract: Taking a digital implant or abutment level digital impression by means of intra-oral, computed tomography or other imaging method provides the restorative doctor and laboratory accurate and effective data for determining the implant position, angulation and locking feature orientation without a physical impression. Such data is correlated with a digital library to produce an output which enables design and fabrication of an accurate restorative device such as a prosthetic tooth or crown. In this way the time-consuming, costly and error prone mechanical replication of the relevant dental anatomy is obviated.

公开(公告)号：US20230360776A1

公开(公告)日：2023-11-09

申请号：US18038112

申请日：2021-11-23

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： HONGXIN CHEN ,  JIAYIN ZHOU ,  SA YUAN LIANG ,  YI FAN

IPC: G06V10/80 ,  G16H30/40 ,  G06V10/82

CPC classification number: G16H30/40 ,  G06V10/80 ,  G06V10/82 ,  G06V2201/031 ,  G06V2201/032

Abstract: A method and system for image feature classification using a NN-based learning algorithms to make a decision about a feature in a medical image or image part. In particular, embodiments may make use of a phase of a multi-phasic image to improve classification accuracy. For instance, embodiments may combine different phases of multiphasic images as training data.

公开(公告)号：US11803967B2

公开(公告)日：2023-10-31

申请号：US17220770

申请日：2021-04-01

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Rahul Venkataramani ,  Deepa Anand ,  Eigil Samset

IPC: G06T7/00 ,  A61B8/08 ,  G06N3/088 ,  G06V10/25 ,  G06V10/98 ,  G06F18/24 ,  G06F18/214 ,  G06N3/045

CPC classification number: G06T7/0014 ,  A61B8/0883 ,  A61B8/5223 ,  G06F18/2148 ,  G06F18/24 ,  G06N3/045 ,  G06N3/088 ,  G06V10/25 ,  G06V10/98 ,  G06T2207/10016 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06V2201/031

Abstract: Various methods and systems are provided for bicuspid valve detection with ultrasound imaging. In one embodiment, a method comprises acquiring ultrasound video of a heart over at least one cardiac cycle, identifying frames in the ultrasound video corresponding to at least one cardiac phase, and classifying a cardiac structure in the identified frames as a bicuspid valve or a tricuspid valve. A generative model such as a variational autoencoder trained on ultrasound image frames at the at least one cardiac phase may be used to classify the cardiac structure. In this way, relatively rare occurrences of bicuspid aortic valves may be automatically detected during regular cardiac ultrasound screenings.

公开(公告)号：US20230335283A1

公开(公告)日：2023-10-19

申请号：US18333420

申请日：2023-06-12

Applicant: FUJIFILM Corporation

Inventor： Yuanzhong LI

IPC: G16H50/20 ,  G06T7/00 ,  G06T11/20 ,  G06V10/764 ,  G06V10/82 ,  G06V10/774

CPC classification number: G16H50/20 ,  G06T7/0012 ,  G06T11/206 ,  G06V10/764 ,  G06V10/82 ,  G06V10/774 ,  G06T2207/30016 ,  G06T2210/41 ,  G06T2207/20084 ,  G06V2201/031 ,  G06T2207/20081 ,  G06T2200/24

Abstract: There is provided an information processing apparatus including: a processor; and a memory connected to or built in the processor, in which the processor is configured to generate a scatter diagram for a machine learning model that receives a plurality of types of input data and outputs output data according to the input data, by plotting, in a two-dimensional space in which two parameters which are set based on the plurality of types of input data are set as a horizontal axis and a vertical axis, marks representing a plurality of samples obtained by inputting the input data to the machine learning model, and display the scatter diagram, the input data, and a type of the output data on a display.

公开(公告)号：US11786309B2

公开(公告)日：2023-10-17

申请号：US17135022

申请日：2020-12-28

Applicant: ADVANCED NEUROMODULATION SYSTEMS, INC.

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

IPC: A61B34/10 ,  G06T7/70 ,  G16H30/40 ,  G06V10/25 ,  A61B5/055 ,  A61B6/03 ,  A61B6/00 ,  G06N3/08 ,  G06T7/00 ,  G06F18/214 ,  A61N1/05

CPC classification number: A61B34/10 ,  A61B5/055 ,  A61B6/032 ,  A61B6/469 ,  A61B6/501 ,  A61B6/5223 ,  A61B6/5247 ,  G06F18/214 ,  G06N3/08 ,  G06T7/0014 ,  G06T7/70 ,  G06V10/25 ,  G16H30/40 ,  A61B2034/107 ,  A61B2505/05 ,  A61N1/0534 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30241 ,  G06V2201/031

Abstract: A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient's brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient's brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.