公开(公告)号：US11990224B2

公开(公告)日：2024-05-21

申请号：US17214442

申请日：2021-03-26

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Hamid Jafarkhani ,  Saeed Karimi-Bidhendi ,  Arash Kheradvar

IPC: G06T7/143 ,  G06F18/21 ,  G06F18/214 ,  G06N3/08 ,  G06N7/01 ,  G06T7/00 ,  G06V10/82 ,  G16H30/40

CPC classification number: G16H30/40 ,  G06F18/2148 ,  G06F18/2185 ,  G06F18/2193 ,  G06N3/08 ,  G06N7/01 ,  G06T7/0012 ,  G06T7/143 ,  G06V10/82 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031

Abstract: Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.

公开(公告)号：US11963820B2

公开(公告)日：2024-04-23

申请号：US16062547

申请日：2016-12-16

Applicant: HOLOGIC, INC.

Inventor： Kevin E. Wilson ,  Thomas L. Kelly

IPC: A61B8/08 ,  A61B5/00 ,  A61B5/0522 ,  A61B5/0536 ,  A61B5/0537 ,  A61B5/055 ,  A61B6/00 ,  A61B6/03 ,  A61B6/50 ,  A61B8/00 ,  G16H50/20

CPC classification number: A61B8/08 ,  A61B5/0073 ,  A61B5/0522 ,  A61B5/0536 ,  A61B5/0537 ,  A61B5/055 ,  A61B6/03 ,  A61B6/482 ,  A61B6/50 ,  A61B6/505 ,  A61B6/5217 ,  A61B6/5294 ,  A61B8/00 ,  G16H50/20 ,  G06F2218/10 ,  G06V2201/031

Abstract: A method of generating a visual representation of a complex medical diagnosis includes receiving a first signal corresponding to a measurement of a patient biological condition. A second signal corresponding to a measurement of a patient performance condition is also received. The first and second signals are processed and a visual representation of a diagnostic assessment is generated. The diagnostic assessment is based at least in part on the patient biological condition and the patient performance condition. The visual representation is marked with the measurement of the patient biological condition and the measurement of the patient performance condition.

公开(公告)号：US11963740B2

公开(公告)日：2024-04-23

申请号：US17098042

申请日：2020-11-13

Applicant: Canon U.S.A., Inc.

Inventor： Yu Zhang

IPC: A61B5/00 ,  G06F18/2431 ,  G06T5/00 ,  G06T7/13 ,  G06T7/73

CPC classification number: A61B5/0066 ,  A61B5/0071 ,  A61B5/0084 ,  A61B5/7271 ,  G06F18/2431 ,  G06T5/005 ,  G06T7/13 ,  G06T7/73 ,  A61B2562/0242 ,  G06T2207/10101 ,  G06T2207/30021 ,  G06T2207/30028 ,  G06T2207/30052 ,  G06T2207/30101 ,  G06V2201/031 ,  G06V2201/034

Abstract: One or more devices, systems, methods and storage mediums for performing optical coherence tomography (OCT) while detecting one or more lumen edges, one or more stent struts, and/or one or more artifacts are provided. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Preferably, the OCT devices, systems methods and storage mediums include or involve a method, such as, but not limited to, for removing the detected one or more artifacts from the image(s).

公开(公告)号：US20240119705A1

公开(公告)日：2024-04-11

申请号：US18285506

申请日：2022-04-01

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Jing Ping Xu ,  Jiayin Zhou ,  Jun Ping Deng ,  William Tao Shi ,  Hua Xie

IPC: G06V10/764 ,  A61B8/08 ,  G06T7/00 ,  G06T7/11 ,  G06T7/143 ,  G06T7/174 ,  G06T11/00 ,  G06V10/22 ,  G06V10/26 ,  G06V10/50 ,  G06V10/82 ,  G16H50/20

CPC classification number: G06V10/764 ,  A61B8/08 ,  A61B8/5223 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T7/174 ,  G06T11/00 ,  G06V10/235 ,  G06V10/267 ,  G06V10/50 ,  G06V10/82 ,  G16H50/20 ,  G06T2207/10016 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20104 ,  G06T2207/30056 ,  G06V2201/031

Abstract: An ultrasound imaging system may acquire an image of a liver. The liver may be segmented from the image. Parameters, such as image homogeneity map, intensity probability chart, and/or speckle size diagram, may be extracted from the liver portion of the image. The parameters may be used to determine whether fatty liver deposits are diffuse or inhomogeneous. In some examples, inhomogeneous regions may be excluded from the calculation of liver fat quantification measurements. In some examples, the inhomogeneous regions may be displayed so that a user may select a region of interest that excludes the inhomogeneous regions to calculate the liver fat quantification measurements.

公开(公告)号：US20240112343A1

公开(公告)日：2024-04-04

申请号：US18255780

申请日：2021-12-02

Applicant: THE ASAN FOUNDATION ,  UNIVERSITY OF ULSAN FOUNDATION FOR INDUSTRY COOPERATION

Inventor： Soo Jin KANG ,  June Goo LEE ,  Hyun Seok MIN ,  Hyung Joo CHO

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/12 ,  G06T7/174 ,  G06T7/62 ,  G06V10/764 ,  G06V10/774 ,  G16H50/20 ,  G16H50/50

CPC classification number: G06T7/0016 ,  G06T7/11 ,  G06T7/12 ,  G06T7/174 ,  G06T7/62 ,  G06V10/764 ,  G06V10/774 ,  G16H50/20 ,  G16H50/50 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30096 ,  G06T2207/30101 ,  G06V2201/031

Abstract: A deep learning-based stent prediction method including: setting as a region of interest, a region in which a procedure is to be performed among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest, obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals, extracting feature information about procedure information of the target patient, obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images, and predicting progress of a stent procedure containing a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.

公开(公告)号：US20240070853A1

公开(公告)日：2024-02-29

申请号：US17821511

申请日：2022-08-23

Applicant: Siemens Healthcare GmbH

Inventor： Youngjin Yoo ,  Eli Gibson ,  Gengyan Zhao ,  Bogdan Georgescu

IPC: G06T7/00 ,  G06T7/62 ,  G06V10/764

CPC classification number: G06T7/0012 ,  G06T7/62 ,  G06V10/765 ,  G06T2207/10028 ,  G06T2207/20221 ,  G06T2207/30016 ,  G06V2201/031

Abstract: Systems and methods for performing a medical imaging analysis task are provided. A plurality of 3D (three dimensional) patches extracted from a 3D input medical image is received. A set of local features is extracted from each of the plurality of 3D patches using a machine learning based local feature extractor network. Global features representing relationships between the sets of local features are determined. A medical imaging analysis task is performed on the 3D input medical image based on the global features. Results of the medical imaging analysis task are output.

公开(公告)号：US11915417B2

公开(公告)日：2024-02-27

申请号：US17240271

申请日：2021-04-26

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

Inventor： Ruibin Feng ,  Zongwei Zhou ,  Jianming Liang

IPC: G06T7/00 ,  G06T15/08 ,  G06T17/10 ,  G06T7/174 ,  G06F18/214 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06F18/2155 ,  G06T7/174 ,  G06T15/08 ,  G06T17/10 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06T2207/30016 ,  G06T2207/30056 ,  G06V2201/031

Abstract: Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specifically configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input; performing a resize operation of the cropped 3D cubes; performing an image reconstruction operation of the resized and cropped 3D cubes to predict the resized whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed.

公开(公告)号：US20240037738A1

公开(公告)日：2024-02-01

申请号：US18336928

申请日：2023-06-16

Applicant: FUJIFILM Corporation

Inventor： Mizuki TAKEI

IPC: G06T7/00 ,  G06V10/25

CPC classification number: G06T7/0012 ,  G06V10/25 ,  G06V2201/031

Abstract: A processor extracts a region of a target organ from a medical image, extracts a region of at least one peripheral organ that is present in a periphery of the target organ from the medical image, derives a positional relationship between the target organ and the peripheral organ, and determines whether or not the target organ is compressed by the peripheral organ based on the positional relationship.

公开(公告)号：US20240020823A1

公开(公告)日：2024-01-18

申请号：US17293250

申请日：2021-03-22

Applicant: DEEPNOID CO., LTD. ,  GIMHAE BIOMEDICAL CENTER ,  JEONGSEOK RESEARCH FOUNDATION

Inventor： Tae-Gyu KIM ,  Hyun-Ju CHOI ,  Hwa-Pyung KIM ,  Hao LI ,  Dae-Woo SEOK ,  Seung-Hoon LEE ,  Woo-Sik CHOI ,  Seung-Hwan LEE

IPC: G06T7/00 ,  G06T7/194 ,  G06T7/11 ,  G06V20/50 ,  G06V10/764 ,  G06V10/774 ,  G16H50/20 ,  G16H30/40 ,  G16H50/50

CPC classification number: G06T7/0012 ,  G06T7/194 ,  G06T7/11 ,  G06V20/50 ,  G06V10/764 ,  G06V10/774 ,  G16H50/20 ,  G16H30/40 ,  G16H50/50 ,  G06T2207/30008 ,  G06T2207/30096 ,  G06T2207/30061 ,  G06T2207/20081 ,  G06V2201/031 ,  G06V2201/033 ,  G06T2207/30204 ,  G06T2207/10116

Abstract: A deep learning-based lung disease diagnosis assistance system according to an embodiment of the present disclosure includes an image input unit inputting a diagnosis target image obtained by capturing a lung image; a bone area removal unit removing a bone area from the diagnosis target image to output a soft tissue image from which the bone area is removed, on the basis of the bone binary model; a lung area extraction unit extracting a lung area from the soft tissue image to output a lung image of the lung area on the basis of a lung segmentation model; and a lung disease diagnosis unit diagnosing whether lung disease is present in the lung image on the basis of a lung disease detection model.

公开(公告)号：US20240008849A1

公开(公告)日：2024-01-11

申请号：US18471251

申请日：2023-09-20

Applicant: TERUMO KABUSHIKI KAISHA

Inventor： Yuki SAKAGUCHI ,  Takanori TOMINAGA

IPC: A61B8/00 ,  G06V10/70 ,  A61B8/12 ,  A61B8/08

CPC classification number: A61B8/463 ,  G06V10/70 ,  A61B8/12 ,  A61B8/0841 ,  A61B8/0891 ,  G06V2201/031 ,  A61B5/0066

Abstract: A medical system includes a catheter that includes a sensor and can be inserted into a luminal organ, a display apparatus, and an image processing apparatus configured to: store a plurality of pieces of support information each related to a medical operation or diagnosis on the organ and associated with a type of an object, generate an image of the organ based on a signal output from the sensor of the catheter, input the generated image to a machine learning model and acquire an output indicating a type of an object that is present in the image, acquire input information indicating a medical operation or diagnosis to be performed, determine one of the pieces of support information corresponding to the type of the object and the medical operation or diagnosis indicated by the input information, and cause the display apparatus to display said one of the pieces of support information.