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

IPC: G06T7/00 ,  G06N20/00 ,  G06N3/08

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.

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

IPC: G16H50/20 ,  G16H30/40 ,  G06T7/00 ,  G06N20/20

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.

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

IPC: G06T7/68 ,  G06N3/04 ,  G06N3/08

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.

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

IPC: G06K9/00 ,  G06T7/68 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US10980502B2

公开(公告)日：2021-04-20

申请号：US16689048

申请日：2019-11-19

Applicant: Shenzhen Keya Medical Technology Corporation

Inventor： Qi Song ,  Ying Xuan Zhi ,  Xiaoxiao Liu ,  Shubao Liu ,  Youbing Yin ,  Yuwei Li ,  Kunlin Cao

IPC: A61B6/00 ,  G06T7/20 ,  G06T7/277 ,  G06T7/00 ,  G06T7/269 ,  G06F16/50

Abstract: The present disclosure relates to a method, storage medium, and system for analyzing an image sequence of a periodic physiological activity. In one implementation, the method includes receiving the image sequence acquired by an imaging device, the image sequence having a plurality of frames, and identifying a feature point in a first frame. The method further includes determining motion vectors for the feature point in the frames of the image sequence. Each motion vector for the feature point is determined based on respective locations of corresponding feature points in frames adjacent to the first frame. The method also includes determining a motion magnitude profile based on the determined motion vectors and determining a phase of each frame in the image sequence based on the motion magnitude profile.

公开(公告)号：US20200297300A1

公开(公告)日：2020-09-24

申请号：US16895573

申请日：2020-06-08

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Qi Song ,  Youbing Yin ,  Shubao Liu ,  Xiaoxiao Liu

IPC: A61B6/00 ,  G06T7/00

Abstract: The disclosure provides a method and device for performing three-dimensional blood vessel reconstruction using projection images of a patient. The computer-implemented method includes receiving a first two-dimensional image of a blood vessel in a first projection direction and a three-dimensional model of the blood vessel. The method further includes determining, by a processor, a first optical path length at a selected position of the blood vessel based on the first two-dimensional image. The method also includes determining, by the processor, a second optical path length at the selected position of the blood vessel in the three-dimensional model. The method additional includes adjusting the three-dimensional model of the blood vessel, based on a comparison of the first optical path length and the second optical path length.

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

IPC: G06T7/11 ,  G06N20/00 ,  G06N3/08 ,  G06T7/174 ,  G06T7/00

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.

公开(公告)号：US10460447B2

公开(公告)日：2019-10-29

申请号：US15842402

申请日：2017-12-14

Applicant: Shenzhen Keya Medical Technology Corporation

Inventor： Qi Song ,  Hanbo Chen ,  Yujie Zhou ,  Youbing Yin ,  Yuwei Li

IPC: G06K9/62 ,  G06T7/143 ,  G06T7/13 ,  G06T7/12 ,  G06T7/194 ,  G06T7/174 ,  G06T7/11

Abstract: Methods and systems for segmenting images having sparsely distributed objects are disclosed. A method may include: predicting object potential areas in the image using a preliminary fully convolutional neural network; segmenting a plurality of sub-images corresponding to the object potential areas in the image using a refinement fully convolutional neural network, wherein the refinement fully convolutional neural network is trained to segment images on a higher resolution compared to a lower resolution utilized by the preliminary fully convolutional neural network; and combining the segmented sub-images to generate a final segmented image.

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

IPC: G06T7/11 ,  G06T7/174 ,  G06T7/00 ,  G06N20/00 ,  G06N3/08

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.

公开(公告)号：US20190080456A1

公开(公告)日：2019-03-14

申请号：US15842402

申请日：2017-12-14

Applicant: Shenzhen Keya Medical Technology Corporation

Inventor： Qi Song ,  Hanbo Chen ,  Yujie Zhou ,  Youbing Yin ,  Yuwei Li

IPC: G06T7/143 ,  G06T7/13 ,  G06T7/12 ,  G06T7/11 ,  G06T7/194 ,  G06T7/174

Abstract: Methods and systems for segmenting images having sparsely distributed objects are disclosed. A method may include: predicting object potential areas in the image using a preliminary fully convolutional neural network; segmenting a plurality of sub-images corresponding to the object potential areas in the image using a refinement fully convolutional neural network, wherein the refinement fully convolutional neural network is trained to segment images on a higher resolution compared to a lower resolution utilized by the preliminary fully convolutional neural network; and combining the segmented sub-images to generate a final segmented image.