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21.发明授权公开(公告)号：US11869142B2
公开(公告)日：2024-01-09
申请号：US17557373
申请日：2021-12-21
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junjie Bai , Shubao Liu , Youbing Yin , Feng Gao , Yue Pan , Qi Song
CPC classification number: G06T17/00 , G06T7/50 , G06T2207/10116 , G06T2207/30101 , G06T2210/41
Abstract: The disclosure provides a method, device and a computer-readable medium for performing three-dimensional blood vessel reconstruction. The device includes an interface configured to receive a single-view two-dimensional image of a blood vessel of a patient, where the single-view two-dimensional image is a projection image acquired in a predetermined projection direction. The device further includes a processor configured to estimate three-dimensional information of the blood vessel from the single-view two-dimensional image using an inference model, and reconstruct a three-dimensional model of the blood vessel based on the three-dimensional information.
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公开(公告)号：US20220284571A1
公开(公告)日：2022-09-08
申请号：US17505917
申请日：2021-10-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Hao-Yu Yang , Junjie Bai , Youbing Yin , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject.
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公开(公告)号：US20220215958A1
公开(公告)日：2022-07-07
申请号：US17568084
申请日：2022-01-04
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Bin Kong , Youbing Yin , Xin Wang , Yi Lu , Haoyu Yang , Junjie Bai , Qi Song
Abstract: The present disclosure relates to training methods for a machine learning model for physiological analysis. The training method may include receiving training data including a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter. The training method further includes training, by at least one processor, an initial machine learning model using the first dataset, and applying, by the at least one processor, the initial machine learning model to the second dataset to generate a third dataset of pseudo-labeled data of the physiological-related parameter. The training method also includes training, by the at least one processor, the machine learning model based on the first dataset and the third dataset, and providing the trained machine learning model for predicting the physiological-related parameter. Thereby, the weakly-labeled dataset may be sufficiently utilized in training of the machine learning model and improve ts p iformance.
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公开(公告)号：US20210279906A1
公开(公告)日：2021-09-09
申请号：US17330557
申请日：2021-05-26
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Zhihui Guo , Youbing Yin , Xin Wang , Yi Lu , Kunlin Cao , Qi Song , Xiaoyang Xu , Bin Ouyang
Abstract: Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes detecting at least one bifurcation of the object using a trained bifurcation learning network based on the image. The method further includes extracting the centerline of the object based on a constraint condition that the centerline passes through the detected bifurcation.
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公开(公告)号：US11069078B2
公开(公告)日：2021-07-20
申请号：US16550093
申请日：2019-08-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Zhihui Guo , Youbing Yin , Xin Wang , Yi Lu , Kunlin Cao , Qi Song , Xiaoyang Xu , Bin Ouyang
Abstract: Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes generating a distance cost image using a trained first learning network based on the image. The method further includes detecting end points of the object using a trained second learning network based on the image. Moreover, the method includes extracting the centerline of the object based on the distance cost image and the end points of the object.
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Patent Agency Ranking
公开(公告)号：US10573005B2
公开(公告)日：2020-02-25
申请号：US16529769
申请日：2019-08-01
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Junjie Bai , Yi Lu , Qi Song , Kunlin Cao
Abstract: Embodiments of the disclosure provide systems and methods for analyzing a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive a learning model and a plurality of model inputs derived from the biomedical image. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to apply the learning model to the plurality of model inputs to analyze the biomedical image. The learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively. The second network is a tree structure network that models a spatial constraint of the tree structure object.
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公开(公告)号：US20190325579A1
公开(公告)日：2019-10-24
申请号：US16392516
申请日：2019-04-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Junjie Bai , Yi Lu , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for segmenting a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive the biomedical image and a learning model. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to extract a plurality of image patches from the biomedical image and apply the learning model to the plurality of image patches to segment the biomedical image. The learning model includes a convolutional network configured to process the plurality of image patches to construct respective feature maps and a tree structure network configured to process the feature maps collectively to obtain a segmentation mask for the tree structure object. The tree structure network models a spatial constraint of the plurality of image patches.
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公开(公告)号：US12094596B2
公开(公告)日：2024-09-17
申请号：US17726039
申请日：2022-04-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Bin Kong , Yi Lu , Xinyu Guo , Hao-Yu Yang , Junjie Bai , Qi Song
Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.
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公开(公告)号：US12086981B2
公开(公告)日：2024-09-10
申请号：US17557449
申请日：2021-12-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Hao-Yu Yang , Youbing Yin , Qi Song
CPC classification number: G06T7/0012 , G06T7/11 , G06T2207/20092 , G06T2207/30096 , G06T2207/30101 , G06T2207/30172
Abstract: Embodiments of the disclosure provide systems and methods for analyzing a medical image containing a vessel structure using a sequential model. An exemplary system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are independently or jointly trained. The exemplary system also includes at least one processor configured to apply the vessel extraction sub-model on the received medical image to extract location information of the vessel structure. The at least one processor also applies the lesion analysis sub-model on the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vessel structure. The at least one processor further outputs the lesion analysis result of the vessel structure.
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公开(公告)号：US11538161B2
公开(公告)日：2022-12-27
申请号：US17015070
申请日：2020-09-08
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Kunlin Cao , Yuwei Li , Junjie Bai , Xiaoyang Xu
Abstract: The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.
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