公开(公告)号：US12062169B2

公开(公告)日：2024-08-13

申请号：US17660422

申请日：2022-04-25

申请人： Hong Kong Applied Science and Technology Research Institute Company Limited

发明人： Xuejian He ,  Lu Wang ,  Ping Shun Leung

IPC分类号： G16H50/20 ,  A61B1/00 ,  A61B1/273 ,  G06T1/20 ,  G06T7/00 ,  G06V10/40

CPC分类号： G06T7/0012 ,  A61B1/000096 ,  A61B1/2736 ,  G06T1/20 ,  G06V10/40 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30092 ,  G06T2207/30096

摘要： A multi-functional, computer-aided gastroscopy system optimized with integrated AI solutions is disclosed. The system makes use of multiple deep-learning neural models to achieve low latency and high-performance requirements for multiple tasks. The optimization is made at three levels: architectural, modular and functional level. At architectural level, the models are designed in such a way that it is able to accomplish HP infection classification and detection of some lesions for one inference in order to reduce computation costs. At modular level, as a sub-model of HP infection classification, the site recognition model is optimized with temporal information. It not only improves the performance of HP infection classification, but also plays important roles for lesion detection and procedure status determination. At functional level, the inference latency is minimized by configuration and resource aware optimization. Also at functional level, the preprocessing is speeded up by image resizing parallelization and unified preprocessing.

公开(公告)号：US12048413B2

公开(公告)日：2024-07-30

申请号：US17252877

申请日：2019-06-20

申请人： AI MEDICAL SERVICE INC.

发明人： Tomohiro Tada ,  Hiroaki Saito ,  Satoki Shichijyo ,  Yuma Endo ,  Kazuharu Aoyama ,  Atsuo Yamada ,  Kentaro Nakagawa ,  Ryu Ishihara ,  Syuntaro Inoue

IPC分类号： G06T7/00 ,  A61B1/00 ,  A61B1/04 ,  A61B1/273 ,  A61B1/31 ,  A61B5/00 ,  A61B6/03 ,  A61B8/13 ,  G06N3/04 ,  G06N3/08 ,  G16H30/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G16H70/60

CPC分类号： A61B1/00009 ,  A61B1/000096 ,  A61B1/00016 ,  A61B1/00045 ,  A61B1/041 ,  A61B5/0022 ,  A61B5/0035 ,  A61B5/4233 ,  A61B5/4238 ,  A61B5/4255 ,  A61B5/7267 ,  A61B6/032 ,  A61B8/13 ,  G06N3/04 ,  G06N3/08 ,  G06T7/0016 ,  G16H30/20 ,  G16H30/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G16H70/60 ,  G06T2207/10068 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30028 ,  G06T2207/30092

摘要： A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network (CNN). A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with a CNN trains the CNN using a first endoscopic image of the digestive organ and at least one final diagnosis result on positivity or negativity to the disease in the digestive organ, a past disease, a severity level, and information corresponding to a site where an image is captured, the final diagnosis result corresponding to the first endoscopic image, and the trained CNN outputs at least one of a probability of the positivity and/or the negativity to the disease, a probability of the past disease, a severity level of the disease, an invasion depth of the disease, and a probability corresponding to the site where the image is captured.

公开(公告)号：US12046368B2

公开(公告)日：2024-07-23

申请号：US17383205

申请日：2021-07-22

申请人： Iterative Scopes, Inc.

发明人： Jonathan Ng ,  Jean-Pierre Schott ,  Daniel Wang

IPC分类号： G16H50/20 ,  A61B1/31 ,  A61B5/00 ,  G06F18/21 ,  G06F18/24 ,  G06N3/08 ,  G06T7/00 ,  G16B20/00 ,  G16B30/00 ,  G16B40/20 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  G16H10/20 ,  G16H10/40

CPC分类号： G16H50/20 ,  A61B1/31 ,  A61B5/7267 ,  A61B5/7275 ,  G06F18/21 ,  G06F18/24 ,  G06N3/08 ,  G06T7/0012 ,  G16B20/00 ,  G16B30/00 ,  G16B40/20 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  G06T2207/10068 ,  G06T2207/20081 ,  G06T2207/30092 ,  G06V2201/03 ,  G16H10/20 ,  G16H10/40

摘要： This specification describes systems and methods for obtaining various patient related data for inflammatory bowel disease (IBD). The methods and systems are configured for using machine learning to determine measurements of various characteristics and provide analysis related to IBD. The methods and systems may also obtain and incorporate electronic health data as well as other relevant data of patients along with endoscopic data to use for prediction IDB progression and recommending treatment.

公开(公告)号：US11969145B2

公开(公告)日：2024-04-30

申请号：US17446095

申请日：2021-08-26

申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

发明人： Zijian Zhang ,  Zhongqian Sun ,  Xinghui Fu ,  Hong Shang ,  Xiaoning Wang ,  Wei Yang

IPC分类号： A61B1/00 ,  G06T7/00

CPC分类号： A61B1/000094 ,  A61B1/000096 ,  A61B1/0005 ,  G06T7/0012 ,  G06T2207/10068 ,  G06T2207/20024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30092 ,  G06T2207/30096

摘要： A medical endoscope image recognition method is provided. In the method, endoscope images are received from a medical endoscope. The endoscope images are filtered with a neural network, to obtain target endoscope images. Organ information corresponding to the target endoscope images is recognized via the neural network. An imaging type of the target endoscope images is identified according to the corresponding organ information with a classification network. A lesion region in the target endoscope images is localized according to an organ part indicated by the organ information. A lesion category of the lesion region in an image capture mode of the medical endoscope corresponding to the imaging type is identified.

公开(公告)号：US20240062439A1

公开(公告)日：2024-02-22

申请号：US18495787

申请日：2023-10-27

申请人： FUJIFILM Corporation

发明人： Takuya TSUTAOKA

IPC分类号： G06T11/20 ,  G06T7/13 ,  G06V10/764 ,  G06T7/11

CPC分类号： G06T11/203 ,  G06T7/13 ,  G06V10/764 ,  G06T7/11 ,  G06V2201/031 ,  G06T2207/10132 ,  G06T2207/10068 ,  G06T2207/30092 ,  G06T2207/20081

摘要： Provided are a display processing apparatus, method, and program for displaying a region of a detection target object in an image in a manner intelligible to a user even if the contour or boundary of the detection target object is unclear. A transmitting/receiving unit (100) and an image generation unit (102), which function as an image acquisition unit, perform an image acquisition process for sequentially acquiring ultrasound images. A region extraction unit (106) extracts a rectangular region including an organ, which is a detection target object, from an acquired ultrasound image. A curve generation unit (108) generates, in the extracted rectangular region, a curve corresponding to the organ in the rectangular region. An image combining unit (109) combines the ultrasound image and the generated curve corresponding to the organ. A display control unit (110) causes a monitor (18) to display the ultrasound image combined with the curve.

公开(公告)号：US20240037733A1

公开(公告)日：2024-02-01

申请号：US18260245

申请日：2022-01-05

申请人： INDUSTRY ACADEMIC COOPERATION FOUNDATION, HALLYM UNIVERSITY

发明人： Chang Seok BANG ,  Jae Jun LEE ,  Bum Joo CHO

IPC分类号： G06T7/00 ,  G06V20/40 ,  G06V20/50 ,  G06V10/94 ,  G06V10/82 ,  A61B1/00

CPC分类号： G06T7/0012 ,  G06V20/49 ,  G06V20/41 ,  G06V20/50 ,  G06V10/95 ,  G06V10/945 ,  G06V10/82 ,  A61B1/000094 ,  A61B1/00016 ,  A61B1/0004 ,  G06T2207/10016 ,  G06T2200/24 ,  G06T2207/10068 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2207/30092 ,  G06T2207/30104

摘要： Provided is a control method for a system for determining a lesion obtained via a real-time image. The control method comprises the steps of: an endoscope device obtaining a stomach endoscopy image; the endoscope device transmitting the obtained stomach endoscopy image to a server; the server determining a lesion included in the stomach endoscopy image, by inputting the stomach endoscopy image into a first artificial intelligence model; when it is determined that a lesion is detected in the stomach endoscopy image, the server obtaining an image including the lesion and transmitting the image to a database of the server; the server determining the type of the lesion included in the image, by inputting the image into a second artificial intelligence model; and when it is determined that a lesion is detected in the stomach endoscopy image, a display device displaying a UI for guiding the location of the lesion in the stomach endoscopy image.

公开(公告)号：US20230368388A1

公开(公告)日：2023-11-16

申请号：US18196126

申请日：2023-05-11

申请人： SHANDONG NORMAL UNIVERSITY

发明人： Jie XUE ,  Dengwang LI ,  Xiyu LIU ,  Qi LI ,  Guanzhong GONG ,  Jianbo WANG ,  Pu HUANG

IPC分类号： G06T7/11 ,  G06T9/00 ,  G06T7/00

CPC分类号： G06T7/11 ,  G06T9/002 ,  G06T7/0012 ,  G06T2207/20132 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2207/30092 ,  G06T2207/20084 ,  G06T2207/10081 ,  G06T2207/10088

摘要： A method and system for segmenting three-dimensional images of pancreases and tumors, including: acquiring 3D images of the pancreas and the tumor and preprocessing same with a soft tissue window to control the intensity value of the image within a set range; cropping all images into block-shaped regions of a set size and feeding same into a trained convolutional neural network model, and when training a network, dynamically adjusting the learning weight between the pancreas and the tumor under the guidance of temperature to learn the features of the pancreas and the tumor; and after performing data preprocessing on the 3D images of the pancreas and labels, using the trained network model for online testing and evaluating, and outputting the segmentation result.

公开(公告)号：US20230342912A1

公开(公告)日：2023-10-26

申请号：US17660422

申请日：2022-04-25

申请人： Hong Kong Applied Science and Technology Research Institute Company Limited

发明人： Xuejian HE ,  Lu WANG ,  Ping Shun LEUNG

IPC分类号： G06T7/00 ,  G06T1/20 ,  G06V10/40 ,  A61B1/00 ,  A61B1/273

CPC分类号： G06T7/0012 ,  G06T1/20 ,  G06V10/40 ,  A61B1/000096 ,  A61B1/2736 ,  G06T2207/30092 ,  G06T2207/30096 ,  G06T2207/20084 ,  G06T2207/20081

摘要： A multi-functional, computer-aided gastroscopy system optimized with integrated AI solutions is disclosed. The system makes use of multiple deep-learning neural models to achieve low latency and high-performance requirements for multiple tasks. The optimization is made at three levels: architectural, modular and functional level. At architectural level, the models are designed in such a way that it is able to accomplish HP infection classification and detection of some lesions for one inference in order to reduce computation costs. At modular level, as a sub-model of HP infection classification, the site recognition model is optimized with temporal information. It not only improves the performance of HP infection classification, but also plays important roles for lesion detection and procedure status determination. At functional level, the inference latency is minimized by configuration and resource aware optimization. Also at functional level, the preprocessing is speeded up by image resizing parallelization and unified preprocessing.

公开(公告)号：US20230274422A1

公开(公告)日：2023-08-31

申请号：US18020291

申请日：2021-09-03

申请人： Given Imaging LTD.

发明人： Dori Peleg

IPC分类号： G06T7/00 ,  G16H30/40 ,  G16H50/20

CPC分类号： G06T7/0012 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10068 ,  G06T2207/30092 ,  G06T2207/30096 ,  G06T2207/20081 ,  A61B1/041

摘要： Systems and methods are disclosed for identifying images that contain polyps. An exemplary method for identifying images includes: accessing images of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, where: each image of the images is suspected to include a polyp and is associated with a probability of containing the polyp, and the images include seed images, where each seed image is associated with one or more images of the images. The image(s) associated with each seed image is identified as suspected to include the same polyp as the associated seed image. The method includes applying a polyp detection system on the seed images to identify seed images which include polyps, where the polyp detection system is applied to each seed image of based on the image(s) associated with the seed image and the probabilities associated with the seed image and with the associated image(s).

公开(公告)号：US20230267679A1

公开(公告)日：2023-08-24

申请号：US18020695

申请日：2021-08-10

申请人： King's College London

发明人： Hongbin Liu ,  George Abrahams ,  Bu'hussain Hayee

IPC分类号： G06T17/00 ,  G06T7/579 ,  G06T7/70 ,  G06T15/04

CPC分类号： G06T17/00 ,  G06T7/579 ,  G06T7/70 ,  G06T15/04 ,  G06T2207/10068 ,  G06T2207/30244 ,  G06T2210/41 ,  G06T2207/30092

摘要： A method of visualising the three-dimensional internal surface of a lumen in real-time comprising using various combinations of image, motion and shape data obtained from an endoscope with a trained neural network and a curved lumen model. The three-dimensional internal surface may be unfolded to form a two-dimensional visualisation.