Patents by Inventor Xuhong Hou

Xuhong Hou 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).

  • Methods of segmenting an abdominal image, computer apparatuses, and storage mediums
    Patent number: 11302014
    Abstract: Methods of segmenting an abdominal image, computer apparatuses and storage mediums. The method includes acquiring an abdominal image to be examined; and classifying pixels in the abdominal image to be examined based on a trained full convolution neural network, and determining a segmented image corresponding to the abdominal image to be examined, wherein the trained full convolution neural network is trained and determined based on a first training set and a second training set, the first training set includes first sample abdominal images and pixel classification label images corresponding to the first sample abdominal images, the second training set includes second sample abdominal images and the number of pixels of second sample abdominal images correspondingly belong to each class. Through the methods herein, the accuracy of the segmentation can be improved.
    Type: Grant
    Filed: November 16, 2018
    Date of Patent: April 12, 2022
    Assignees: SHANGHAI SIXTH PEOPLE'S HOSPITAL, SHANGHAI JIAO TONG UNIVERSITY
    Inventors: Weiping Jia, Bin Sheng, Huating Li, Siyuan Pan, Xuhong Hou, Liang Wu
  • METHODS OF SEGMENTING AN ABDOMINAL IMAGE, COMPUTER APPARATUSES, AND STORAGE MEDIUMS
    Publication number: 20210366125
    Abstract: Methods of segmenting an abdominal image, computer apparatuses and storage mediums. The method includes acquiring an abdominal image to be examined; and classifying pixels in the abdominal image to be examined based on a trained full convolution neural network, and determining a segmented image corresponding to the abdominal image to be examined, wherein the trained full convolution neural network is trained and determined based on a first training set and a second training set, the first training set includes first sample abdominal images and pixel classification label images corresponding to the first sample abdominal images, the second training set includes second sample abdominal images and the number of pixels of second sample abdominal images correspondingly belong to each class. Through the methods herein, the accuracy of the segmentation can be improved.
    Type: Application
    Filed: November 16, 2018
    Publication date: November 25, 2021
    Inventors: Weiping Jia, Bin Sheng, Huating Li, Siyuan Pan, Xuhong Hou, Liang WU