Patents by Inventor Junjie Bai

Junjie Bai 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: 20200410678
    Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.
    Type: Application
    Filed: September 10, 2020
    Publication date: December 31, 2020
    Applicant: BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Feng Gao, Hanbo Chen, Shanhui Sun, Junjie Bai, Zheng Te, Youbing Yin
  • Publication number: 20200402237
    Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.
    Type: Application
    Filed: September 6, 2020
    Publication date: December 24, 2020
    Inventors: Qi Song, Hanbo Chen, Zheng Te, Youbing Yin, Junjie Bai, Shanhui Sun
  • 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: 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
  • Patent number: 10867384
    Abstract: A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.
    Type: Grant
    Filed: June 2, 2018
    Date of Patent: December 15, 2020
    Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Qi Song, Shanhui Sun, Hanbo Chen, Junjie Bai, Feng Gao, Youbing Yin
  • 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: 10803579
    Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition of the patient and parameters associated with the medical condition based on the medical image. The at least one processor is further configured to construct the diagnosis report based on the medical image, wherein the diagnosis report includes at least one view of the medical image and a description of the medical condition using the parameters. The system also includes a display configured to display the diagnosis report.
    Type: Grant
    Filed: October 8, 2018
    Date of Patent: October 13, 2020
    Assignee: BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Hanbo Chen, Zheng Te, Youbing Yin, Junjie Bai, Shanhui Sun
  • Patent number: 10803581
    Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on medical images of a patient. An exemplary system includes a communication interface configured to receive the medical images acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to receive a user selection of at least one medical image in at least one view. The at least one processor is further configured to automatically generate keywords describing the selected medical image based on a learning network including a convolutional neural network and a recursive neural network connected in series. The at least one processor is also configured to receive a keyword selection among the generated keywords and generate the report based on the keyword selection. The exemplary system additionally includes a display configured to display the selected medical image and the report.
    Type: Grant
    Filed: November 4, 2018
    Date of Patent: October 13, 2020
    Assignee: BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Feng Gao, Hanbo Chen, Shanhui Sun, Junjie Bai, Zheng Te, Youbing Yin
  • 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
  • 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: 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
  • 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
  • Publication number: 20190325579
    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.
    Type: Application
    Filed: April 23, 2019
    Publication date: October 24, 2019
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Yi Lu, Qi Song
  • Patent number: 10430949
    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.
    Type: Grant
    Filed: April 23, 2019
    Date of Patent: October 1, 2019
    Assignee: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Xin Wang, Youbing Yin, Junjie Bai, Yi Lu, Qi Song
  • 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