Patents by Inventor Kunlin Cao

Kunlin Cao has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200402666
    Abstract: A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.
    Type: Application
    Filed: June 19, 2020
    Publication date: December 24, 2020
    Applicant: BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD.
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Qi Song, Kunlin Cao, Yi Lu, Feng Gao
  • Publication number: 20200402239
    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.
    Type: Application
    Filed: September 8, 2020
    Publication date: December 24, 2020
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Kunlin Cao, Yuwei Li, Junjie Bai, Xiaoyang Xu
  • Publication number: 20200349697
    Abstract: Embodiments of the disclosure provide systems and methods for detecting an intracerebral hemorrhage (ICH). The system includes a communication interface configured to receive a sequence of image slices and an end-to-end multi-task learning model. The sequence of image slices is the head scan images of a subject acquired by an image acquisition device. The end-to-end multi-task learning model includes an encoder, a bi-directional Convolutional Recurrent Neural Network (ConvRNN), a decoder, and a classifier. The system further includes at least one processor configured to extract feature maps from each image slice using the encoder, capture contextual information between adjacent image slices using the bi-directional ConvRNN, and detect the ICH of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain an ICH region based on the extracted feature maps of the image slice.
    Type: Application
    Filed: April 28, 2020
    Publication date: November 5, 2020
    Applicant: CURACLOUD CORPORATION
    Inventors: Feng Gao, Youbing Yin, Danfeng Guo, Pengfei Zhao, Xin Wang, Hao-Yu Yang, Yue Pan, Yi Lu, Junjie Bai, Kunlin Cao, Qi Song, Xiuwen Yu
  • Publication number: 20200349706
    Abstract: Embodiments of the disclosure provide systems and methods for biomedical image analysis. A method may include receiving a plurality of unannotated biomedical images, including a first image and a second image. The method may also include determining that the first image is in a first view and the second image is in a second view. The method may further include assigning the first image to a first processing path for the first orientation. The method may additionally include assigning the second image to a second processing path for the second view. The method may also include processing the first image in the first processing path in parallel with processing the second image in the second processing path. The first path may share processing parameters with the second path. The method may further include providing a diagnostic output based on the processing of the first image and the second image.
    Type: Application
    Filed: April 29, 2020
    Publication date: November 5, 2020
    Applicant: CURACLOUD CORPORATION
    Inventors: Feng Gao, Hao-Yu Yang, Youbing Yin, Yue Pan, Xin Wang, Junjie Bai, Yi Wu, Kunlin Cao, Qi Song
  • Patent number: 10803583
    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.
    Type: Grant
    Filed: August 7, 2018
    Date of Patent: October 13, 2020
    Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Kunlin Cao, Yuwei Li, Junjie Bai, Xiaoyang Xu
  • Publication number: 20200311485
    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.
    Type: Application
    Filed: March 23, 2020
    Publication date: October 1, 2020
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Qi Song, Junjie Bai, Yi Lu, Yi Wu, Feng Gao, Kunlin Cao
  • Patent number: 10769791
    Abstract: Embodiments of the disclosure provide systems and methods for segmenting a medical image. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system also includes a memory configured to store a plurality of learning networks jointly trained using first training images of a first imaging modality and second training images of a second imaging modality. The system further includes a processor, configured to segment the medical image using a segmentation network selected from the plurality of learning networks.
    Type: Grant
    Filed: September 21, 2018
    Date of Patent: September 8, 2020
    Assignee: BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Shanhui Sun, Youbing Yin, Kunlin Cao
  • Publication number: 20200098124
    Abstract: The present disclosure provides a prediction method for a healthy radius of a blood vessel path, a prediction method for candidate stenosis of a blood vessel path, and a blood vessel stenosis degree prediction device. The prediction method for a healthy radius includes: obtaining a blood vessel radius of the blood vessel path; by a processor, detecting a radius peak of the blood vessel radius of the blood vessel path; and by the processor, predicting the healthy radius of the blood vessel path by performing a regression on the radius peak of the blood vessel radius. The blood vessel stenosis degree prediction device can, in certain embodiments, automatically determine the candidate stenosis and detect the degree of stenosis for the candidate stenosis range, significantly reduce the computation load, improve the detection efficiency and effectively avoid missed detection.
    Type: Application
    Filed: September 24, 2019
    Publication date: March 26, 2020
    Applicant: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Yuwei Li, Yi Lu, Kunlin Cao, Qi Song
  • Publication number: 20200085395
    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.
    Type: Application
    Filed: November 19, 2019
    Publication date: March 19, 2020
    Inventors: Qi SONG, Ying Xuan ZHI, Xiaoxiao LIU, Shubao LIU, Youbing YIN, Yuwei LI, Kunlin CAO
  • Publication number: 20200065374
    Abstract: Embodiments of the disclosure provide systems and methods for processing unstructured texts in a medical record. A disclosed system includes at least one processor configured to determine a plurality of word representations of an unstructured text and tag entities in the unstructured text by performing a named entity recognition task on the plurality of word representations. The at least one processor is further configured to determine position embeddings based on positions of words in the unstructured text relative to positions of the tagged entities and concatenate the plurality of word representations with the position embeddings. The at least one processor is also configured to determine relation labels between pairs of tagged entities by performing a relationship extraction task on the concatenated word representations and position embeddings.
    Type: Application
    Filed: August 19, 2019
    Publication date: February 27, 2020
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Feng Gao, Changsheng Liu, Yue Pan, Youbing Yin, Kunlin Cao, Qi Song
  • Publication number: 20200065989
    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.
    Type: Application
    Filed: August 23, 2019
    Publication date: February 27, 2020
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Junjie Bai, Zhihui Guo, Youbing Yin, Xin Wang, Yi Lu, Kunlin Cao, Qi Song, Xiaoyang Xu, Bin Ouyang
  • Patent number: 10573005
    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.
    Type: Grant
    Filed: August 1, 2019
    Date of Patent: February 25, 2020
    Assignee: Shenzhen Keya Medical Technology Corporation
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Yi Lu, Qi Song, Kunlin Cao
  • Patent number: 10548552
    Abstract: The present disclosure is directed to a method and device for generating anatomical labels for a physiological tree structure. The method may include receiving a 3D model and a 3D skeleton line of the physiological tree structure. The 3D model is restructured based on medical image data of the physiological tree structure acquired by an imaging device. The method further includes selecting at least one level from extracting geometrical features from a pool of selectable levels. The method also includes extracting, by a processor, geometrical features from the 3D model of the physiological tree structure along the 3D skeleton line at the selected at least one level. The method also includes generating, by the processor, anatomical labels for the physiological tree structure using a trained learning network based on the extracted geometrical features.
    Type: Grant
    Filed: August 29, 2018
    Date of Patent: February 4, 2020
    Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Dan Wu, Xin Wang, Youbing Yin, Yuwei Li, Kunlin Cao, Qi Song, Bin Ouyang, Shuyi Liang
  • Patent number: 10499867
    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 determining local motions for pixels in each frame of the image sequence. The local motion for a pixel may be determined using corresponding pixels in frames adjacent to the frame to which the pixel belongs. The method further includes determining principal motions for the plurality of frames based on the local motions; determining a motion magnitude profile based on the principal motions; and determining the phase of each frame in the image sequence based on the motion magnitude profile.
    Type: Grant
    Filed: January 8, 2018
    Date of Patent: December 10, 2019
    Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xiaoxiao Liu, Shubao Liu, Bin Ma, Kunlin Cao, Youbing Yin, Yuwei Li, Qian Zhao, Qi Song
  • Publication number: 20190371433
    Abstract: The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a set of encoders to the model inputs. Each encoder is configured to extract features from the model input at a corresponding position. The method also includes applying, by the processor, a tree structured network to the extracted features. The tree structured network has a plurality of nodes each connected to one or more of the encoders, and information propagates among the nodes of the tree structured network according to spatial constraints of the anatomical tree structure. The method additionally includes providing an output of the tree structured network as an analysis result of the anatomical tree structure analysis.
    Type: Application
    Filed: August 1, 2019
    Publication date: December 5, 2019
    Applicant: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Xin Wang, Youbing Yin, Kunlin Cao, Junjie Bai, Yi Lu, Bin Ouyang, Qi Song
  • Publication number: 20190362494
    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.
    Type: Application
    Filed: August 7, 2018
    Publication date: November 28, 2019
    Inventors: Xin Wang, Youbing Yin, Kunlin Cao, Yuwei Li, Junjie Bai, Xiaoyang Xu
  • Publication number: 20190355120
    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.
    Type: Application
    Filed: August 1, 2019
    Publication date: November 21, 2019
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Yi Lu, Qi Song, Kunlin Cao
  • Patent number: 10431328
    Abstract: The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method may begin with receiving a task of the anatomical tree structure analysis. Then, a set of positions in the anatomical tree structure may be set or received, by a processor, as the sampling positions for model inputs and model outputs. Then model inputs may be determined, by the processor, at the sampling positions on the basis of the task. An encoder may be selected, by the processor, for each position of the set of positions on the basis of the task. The encoder may be configured to receive a model input at each position and extract features for the corresponding position. After that, a tree structured recurrent neural network (RNN) may be constructed by the processor with nodes corresponding to the set of positions and connected with the respective encoders.
    Type: Grant
    Filed: September 21, 2018
    Date of Patent: October 1, 2019
    Assignee: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Xin Wang, Youbing Yin, Kunlin Cao, Junjie Bai, Yi Lu, Bin Ouyang, Qi Song
  • Publication number: 20190209113
    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 determining local motions for pixels in each frame of the image sequence. The local motion for a pixel may be determined using corresponding pixels in frames adjacent to the frame to which the pixel belongs. The method further includes determining principal motions for the plurality of frames based on the local motions; determining a motion magnitude profile based on the principal motions; and determining the phase of each frame in the image sequence based on the motion magnitude profile.
    Type: Application
    Filed: January 8, 2018
    Publication date: July 11, 2019
    Inventors: Xiaoxiao LIU, Shubao LIU, Bin MA, Kunlin CAO, Youbing YIN, Yuwei LI, Qian ZHAO, Qi SONG
  • Publication number: 20190192096
    Abstract: The present disclosure is directed to a method and device for generating anatomical labels for a physiological tree structure. The method may include receiving a 3D model and a 3D skeleton line of the physiological tree structure. The 3D model is restructured based on medical image data of the physiological tree structure acquired by an imaging device. The method further includes selecting at least one level from extracting geometrical features from a pool of selectable levels. The method also includes extracting, by a processor, geometrical features from the 3D model of the physiological tree structure along the 3D skeleton line at the selected at least one level. The method also includes generating, by the processor, anatomical labels for the physiological tree structure using a trained learning network based on the extracted geometrical features.
    Type: Application
    Filed: August 29, 2018
    Publication date: June 27, 2019
    Applicant: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Dan Wu, Xin Wang, Youbing Yin, Yuwei Li, Kunlin Cao, Qi Song, Bin Ouyang, Shuyi Liang