Patents by Inventor Shaohua Kevin Zhou

Shaohua Kevin Zhou 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).

  • Patent number: 11328412
    Abstract: Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.
    Type: Grant
    Filed: January 9, 2018
    Date of Patent: May 10, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Daguang Xu, Zhoubing Xu, Shun Miao, Dong Yang, He Zhang
  • Patent number: 11311270
    Abstract: An anatomical structure is detected (110) in a volume of ultrasound data by identifying (150) the anatomical structure in another volume of ultrasound data and generating (155) an image of the anatomical structure and an anatomical landmark. A group of images are generated (130) of the original volume and compared (140) to the image of the other volume. An image of the group of images is selected (150) as including the anatomical structure based on the comparison.
    Type: Grant
    Filed: July 2, 2015
    Date of Patent: April 26, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Jin-hyeong Park, Michal Sofka, Shaohua Kevin Zhou
  • Patent number: 11229377
    Abstract: A method of visualizing spinal nerves includes receiving a 3D image volume depicting a spinal cord and a plurality of spinal nerves. For each spinal nerve, a 2D spinal nerve image is generated by defining a surface within the 3D volume comprising the spinal nerve. The surface is curved such that it passes through the spinal cord while encompassing the spinal nerve. Then, the 2D spinal nerve images are generated based on voxels on the surface included in the 3D volume. A visualization of the 2D spinal images is presented in a graphical user interface that allows each 2D spinal image to be viewed simultaneously.
    Type: Grant
    Filed: July 12, 2019
    Date of Patent: January 25, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Atilla Peter Kiraly, David Liu, Shaohua Kevin Zhou, Dorin Comaniciu, Gunnar Krüger
  • Patent number: 11210779
    Abstract: Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.
    Type: Grant
    Filed: September 7, 2018
    Date of Patent: December 28, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Zhoubing Xu, Sasa Grbic, Shaohua Kevin Zhou, Philipp Hölzer, Grzegorz Soza
  • Patent number: 11185231
    Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.
    Type: Grant
    Filed: March 25, 2020
    Date of Patent: November 30, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Florin Cristian Ghesu, Yefeng Zheng, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Patent number: 11120582
    Abstract: An apparatus and method for coupled medical image formation and medical image signal recovery using a dual domain network is disclosed. The dual-domain network includes a first deep neural network (DNN) to perform signal recovery in a sensor signal domain and a second DNN to perform signal recovery in an image domain. A sensor signal is acquired by a sensor of a medical imaging device. A refined sensor signal is generated from the received sensor signal using the first DNN. A first reconstructed medical image is generated from the received sensor signal. A second reconstructed medical image is generated from the refined sensor signal generated by the first DNN. An enhanced medical image is generated based on the both the first reconstructed medical image and the second reconstructed medical image using the second DNN. The enhanced medical image generated by the second DNN is displayed.
    Type: Grant
    Filed: July 31, 2019
    Date of Patent: September 14, 2021
    Assignee: Z2SKY TECHNOLOGIES INC.
    Inventors: Shaohua Kevin Zhou, Haofu Liao, Wei-An Lin
  • Patent number: 11055847
    Abstract: Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.
    Type: Grant
    Filed: March 18, 2020
    Date of Patent: July 6, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Daguang Xu, Zhoubing Xu, Dong Yang
  • Publication number: 20210110135
    Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.
    Type: Application
    Filed: November 24, 2020
    Publication date: April 15, 2021
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Hui Ding, Bogdan Georgescu, Mehmet Akif Gulsun, Tae Soo Kim, Atilla Peter Kiraly, Xiaoguang Lu, Jin-hyeong Park, Puneet Sharma, Shanhui Sun, Daguang Xu, Zhoubing Xu, Yefeng Zheng
  • Publication number: 20210035338
    Abstract: An apparatus and method for coupled medical image formation and medical image signal recovery using a dual domain network is disclosed. The dual-domain network includes a first deep neural network (DNN) to perform signal recovery in a sensor signal domain and a second DNN to perform signal recovery in an image domain. A sensor signal is acquired by a sensor of a medical imaging device. A refined sensor signal is generated from the received sensor signal using the first DNN. A first reconstructed medical image is generated from the received sensor signal. A second reconstructed medical image is generated from the refined sensor signal generated by the first DNN. An enhanced medical image is generated based on the both the first reconstructed medical image and the second reconstructed medical image using the second DNN. The enhanced medical image generated by the second DNN is displayed.
    Type: Application
    Filed: July 31, 2019
    Publication date: February 4, 2021
    Applicant: Z2Sky Technologies Inc.
    Inventors: Shaohua Kevin Zhou, Haofu Liao, Wei-An Lin
  • Patent number: 10910099
    Abstract: Medical image data may be applied to a machine-learned network learned on training image data and associated image segmentations, landmarks, and view classifications to classify a view of the medical image data, detect a location of one or more landmarks in the medical image data, and segment a region in the medical image data based on the application of the medical image data to the machine-learned network. The classified view, the segmented region, or the location of the one or more landmarks may be output.
    Type: Grant
    Filed: February 11, 2019
    Date of Patent: February 2, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Zhoubing Xu, Yuankai Huo, Jin-hyeong Park, Sasa Grbic, Shaohua Kevin Zhou
  • Patent number: 10878219
    Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.
    Type: Grant
    Filed: July 19, 2017
    Date of Patent: December 29, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Hui Ding, Bogdan Georgescu, Mehmet Akif Gulsun, Tae Soo Kim, Atilla Peter Kiraly, Xiaoguang Lu, Jin-hyeong Park, Puneet Sharma, Shanhui Sun, Daguang Xu, Zhoubing Xu, Yefeng Zheng
  • Patent number: 10779785
    Abstract: A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.
    Type: Grant
    Filed: July 12, 2018
    Date of Patent: September 22, 2020
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Lucian Mihai Itu, Laszlo Lazar, Siqi Liu, Olivier Pauly, Philipp Seegerer, Iulian Ionut Stroia, Alexandru Turcea, Anamaria Vizitiu, Daguang Xu, Shaohua Kevin Zhou
  • Patent number: 10748277
    Abstract: Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.
    Type: Grant
    Filed: September 9, 2016
    Date of Patent: August 18, 2020
    Assignees: Siemens Healthcare GmbH, The Johns Hopkins University
    Inventors: Shaohua Kevin Zhou, David Liu, Berthold Kiefer, Atilla Peter Kiraly, Benjamin L. Odry, Robert Grimm, Li Pan, Ihab Kamel
  • Publication number: 20200242405
    Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.
    Type: Application
    Filed: March 25, 2020
    Publication date: July 30, 2020
    Inventors: Bogdan Georgescu, Florin Cristian Ghesu, Yefeng Zheng, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Patent number: 10709394
    Abstract: A method and apparatus for automated reconstruction of a 3D computed tomography (CT) volume from a small number of X-ray images is disclosed. A sparse 3D volume is generated from a small number of x-ray images using a tomographic reconstruction algorithm. A final reconstructed 3D CT volume is generated from the sparse 3D volume using a trained deep neural network. A 3D segmentation mask can also be generated from the sparse 3D volume using the trained deep neural network.
    Type: Grant
    Filed: January 15, 2018
    Date of Patent: July 14, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Shaohua Kevin Zhou, Sri Venkata Anirudh Nanduri, Jin-hyeong Park, Haofu Liao
  • Publication number: 20200219259
    Abstract: Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.
    Type: Application
    Filed: March 18, 2020
    Publication date: July 9, 2020
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Daguang Xu, Zhoubing Xu, Dong Yang
  • Patent number: 10643105
    Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.
    Type: Grant
    Filed: August 29, 2017
    Date of Patent: May 5, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Florin Cristian Ghesu, Yefeng Zheng, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Patent number: 10636141
    Abstract: Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.
    Type: Grant
    Filed: January 11, 2018
    Date of Patent: April 28, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Daguang Xu, Zhoubing Xu, Dong Yang
  • Patent number: 10627470
    Abstract: A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.
    Type: Grant
    Filed: December 8, 2016
    Date of Patent: April 21, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Xiao Chen, Boris Mailhe, Qiu Wang, Shaohua Kevin Zhou, Yefeng Zheng, Xiaoguang Lu, Puneet Sharma, Benjamin L. Odry, Bogdan Georgescu, Mariappan S. Nadar
  • Patent number: 10607342
    Abstract: Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.
    Type: Grant
    Filed: June 16, 2017
    Date of Patent: March 31, 2020
    Assignee: Siemenes Healthcare GmbH
    Inventors: Li Zhang, Shanhui Sun, Shaohua Kevin Zhou, Daguang Xu, Zhoubing Xu, Tommaso Mansi, Ying Chi, Yefeng Zheng, Pavlo Dyban, Nora Hünemohr, Julian Krebs, David Liu