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公开(公告)号：US10430949B1
公开(公告)日：2019-10-01
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：US11748902B2
公开(公告)日：2023-09-05
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
CPC classification number: G06T7/68 , G06N3/045 , G06N3/08 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101 , G06T2207/30172
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：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：US20230098121A1
公开(公告)日：2023-03-30
申请号：US17489682
申请日：2021-09-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Feng Gao , Hao-Yu Yang , Yue Pan , Youbing Yin , Qi Song
IPC: G16H50/20 , G16H10/60 , G16H50/30 , G16H50/50 , G16H30/40 , G06F16/23 , A61B5/00 , A61B5/02 , A61B5/107
Abstract: The disclosure relates to a method for prognosis management based on medical information of a patient, a device, and a medium. The method includes acquiring, by a processor, medical information of the patient at a first time. The method further includes receiving the medical information of the patient at a first time. The method may further include predicting, by a processor, a progression condition of an object associated with the patient at a second time based on the acquired medical information of the first time. The progression condition is indicative of a prognosis risk, and the second time is after the first time. The method may also include outputting the predicted progression condition to an information management system. The method is helpful for users to understand the potential prognosis risk of the object at the second time to aid users in making treatment decisions.
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公开(公告)号：US20220392059A1
公开(公告)日：2022-12-08
申请号：US17558756
申请日：2021-12-22
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Bin KONG , Xin WANG , Youbing YIN , Hao-Yu YANG , Yi LU , Xinyu GUO , Qi SONG
Abstract: Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.
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Patent Agency Ranking
公开(公告)号：US20220344033A1
公开(公告)日：2022-10-27
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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17.发明申请公开(公告)号：US20220198226A1
公开(公告)日：2022-06-23
申请号：US17692337
申请日：2022-03-11
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Qi Song , Junjie Bai , Yi Lu , Yi Wu , Feng Gao , Kunlin Cao
Abstract: Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object, by a processor, using a reinforcement learning network configured to predict movement of a virtual agent that traces the centerline in the image. The reinforcement learning network is further configured to perform at least one auxiliary task that detects a bifurcation in a trajectory of the object. The reinforcement learning network is trained by maximizing a cumulative reward and minimizing an auxiliary loss of the at least one auxiliary task. Additionally, the method includes displaying the centerline of the object.
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18.发明授权公开(公告)号：US11341631B2
公开(公告)日：2022-05-24
申请号：US16028389
申请日：2018-07-05
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Qi Song , Shanhui Sun , Feng Gao , Junjie Bai , Hanbo Chen , Youbing Yin
Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.
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19.发明授权公开(公告)号：US11308362B2
公开(公告)日：2022-04-19
申请号：US16827613
申请日：2020-03-23
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Qi Song , Junjie Bai , Yi Lu , Yi Wu , Feng Gao , Kunlin Cao
Abstract: Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object by tracing a sequence of patches with a virtual agent. For each patch other than the initial patch, the method determines a current patch based on the position and action of the virtual agent at a previous patch. The method further determines a policy function and a value function based on the current patch using a trained learning network, which includes an encoder followed by a first learning network and a second learning network. The learning network is trained by maximizing a cumulative reward. The method also determines the action of the virtual agent at the current patch. Additionally, the method displays the centerline of the object.
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公开(公告)号：US20210374950A1
公开(公告)日：2021-12-02
申请号：US17121595
申请日：2020-12-14
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Feng Gao , Zhenghan Fang , Yue Pan , Junjie Bai , Youbing Yin , Hao-Yu Yang , Kunlin Cao , Qi Song
Abstract: The disclosure relates to systems and methods for vessel image analysis. The method includes receiving a set of images along a vessel acquired by a medical imaging device, and determining a sequence of centerline points along the vessel and a sequence of image patches at the respective centerline points based on the set of images. The method further includes detecting plaques based on the sequence of image patches using a first learning network. The first learning network includes an encoder configured to extract feature maps based on the sequence of image patches and a plaque range generator configured to generate a start position and an end position of each plaque based on the extracted feature maps. The method also includes classifying each detected plaque and determining a stenosis degree for the detected plaque, using a second learning network reusing at least part of the parameters of the first learning network and the extracted feature maps.
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