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公开(公告)号：US12094188B2
公开(公告)日：2024-09-17
申请号：US17565274
申请日：2021-12-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junhuan Li , Ruoping Li , Ling Hou , Pengfei Zhao , Yuwei Li , Kunlin Cao , Qi Song
IPC: G06V10/774 , G06T7/00 , G06V10/776 , G06V10/82
CPC classification number: G06V10/7747 , G06T7/0012 , G06V10/776 , G06V10/82 , G06T2207/10081 , G06T2207/30048
Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.
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公开(公告)号：US20230177677A1
公开(公告)日：2023-06-08
申请号：US17741654
申请日：2022-05-11
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Shaofeng Yuan , Xiaomeng Huang , Tong Zheng , Yuwei Li , Kunlin Cao , Liwei Wang
CPC classification number: G06T7/0012 , G06T7/11 , G06T7/60 , G16H30/40 , G16H40/63 , G06T2207/20021 , G06T2207/30101 , G06T2207/20081 , G06T2207/20132 , G06T2207/30008 , G06T2207/10012 , G06T2200/04 , G06T2207/10072 , G06T2207/30061 , G06T2207/20084
Abstract: The present disclosure relates to a method, a device and a medium for performing vessel segmentation in a medical image. The method may comprise acquiring a medical image for vessel segmentation containing multiple parts, each of which contains vessels with different structural attributes. The method may comprise dividing the medical image into sub-medical images according to the parts by using a processor. The method may comprise determining individual vessel segmentation result for each part by means of using the vessel segmentation model corresponding to the part based on the sub-medical image of the part by using the processor. The method may comprise obtaining a vessel segmentation result of the medical image by means of fusing the individual vessel segmentation results of the sub-medical images of the parts by the processor. By applying the method and the device, vessel segmentation models are adapted differentially for the individual parts so as to segment the vessels of the individual parts respectively, so that the entire vessel segmentation process is fast, effective, and accurate.
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公开(公告)号：US10580526B2
公开(公告)日：2020-03-03
申请号：US15870811
申请日：2018-01-12
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Bin Ma , Xiaoxiao Liu , Yujie Zhou , Youbing Yin , Yuwei Li , Shubao Liu , Xiaoyang Xu , Qi Song
Abstract: The present disclosure relates to a device, a system, and a computer-readable medium for calculating vessel flow parameters based on angiography. In one implementation, the device includes a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: selecting a plurality of template frames from the angiographic images to generate a 3D model for a vessel; determining a start frame and an end frame in the plurality of angiographic images showing a contrast filling process; determining corresponding locations of front ends of the contrast in the start frame and the end frame in the 3D model of the vessel; calculating a vessel volume between the determined locations of the front ends in the 3D model; and determining an average blood flow rate based on the calculated volume, and a time interval between the start frame and the end frame.
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公开(公告)号：US20190362494A1
公开(公告)日：2019-11-28
申请号：US16056535
申请日：2018-08-07
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 determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.
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公开(公告)号：US10803583B2
公开(公告)日：2020-10-13
申请号：US16056535
申请日：2018-08-07
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 determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.
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Patent Agency Ranking
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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公开(公告)号：US20220366679A1
公开(公告)日：2022-11-17
申请号：US17565274
申请日：2021-12-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Junhuan Li , Ruoping LI , Ling Hou , Pengfei Zhao , Yuwei Li , Kunlin Cao , Qi Song
IPC: G06V10/774 , G06V10/776 , G06T7/00 , G06V10/82
Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.
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公开(公告)号：US11494908B2
公开(公告)日：2022-11-08
申请号：US17408321
申请日：2021-08-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Ruoping Li , Pengfei Zhao , Junhuan Li , Bin Ouyang , Yuwei Li , Kunlin Cao , Qi Song
Abstract: The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.
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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
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.
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10.发明授权公开(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
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.
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