Patents by Inventor Antong Chen

Antong Chen 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: 11030750
    Abstract: Approaches for the automatic segmentation of magnetic resonance (MR) images. Machine learning models segment images to identify image features in consecutive frames at different levels of resolution. A neural network block is applied to groups of MR images to produce primary feature maps at two or more levels of resolution. The images in a given group of MR images may correspond to a cycle and have a temporal order. A second RNN block is applied to the primary feature maps to produce two or more output tensors at corresponding levels of resolution. A segmentation block is applied to the two or more output tensors to produce a probability map for the MR images. The first neural network block may be a convolutional neural network (CNN) block. The second neural network block may be a convolutional long short-term (LSTM) block.
    Type: Grant
    Filed: May 30, 2019
    Date of Patent: June 8, 2021
    Assignees: Merck Sharp & Dohme Corp., MSD International GmbH
    Inventors: Antong Chen, Dongqing Zhang, Ilknur Icke, Belma Dogdas, Sarayu Parimal
  • Publication number: 20210056703
    Abstract: A system and method are disclosed for segmenting a set of two-dimensional CT slices corresponding to a lesion. In an embodiment, for each of at least a subset of the set of CT slices, the system inputs the CT slice into a plurality of branches of a trained segmentation block. Each branch of the segmentation block includes a convolutional neural network (CNN) with filters at a different scale, and produces one or more levels of output. The system generates, for each CT slice in the subset, feature maps for each level of output. The system generates a segmentation of each CT slice in the subset based on the feature maps of each level of output. The system aggregates the segmentations of each slice in the subset to generate a three-dimensional segmentation of the lesion. The system transmits data representing the three-dimensional segmentation to a user interface for display.
    Type: Application
    Filed: April 14, 2020
    Publication date: February 25, 2021
    Inventors: Antong Chen, Gregory Goldmacher, Bo Zhou
  • Publication number: 20200111214
    Abstract: Approaches for the automatic segmentation of magnetic resonance (MR) images. Machine learning models segment images to identify image features in consecutive frames at different levels of resolution. A neural network block is applied to groups of MR images to produce primary feature maps at two or more levels of resolution. The images in a given group of MR images may correspond to a cycle and have a temporal order. A second RNN block is applied to the primary feature maps to produce two or more output tensors at corresponding levels of resolution. A segmentation block is applied to the two or more output tensors to produce a probability map for the MR images. The first neural network block may be a convolutional neural network (CNN) block. The second neural network block may be a convolutional long short-term (LSTM) block.
    Type: Application
    Filed: May 30, 2019
    Publication date: April 9, 2020
    Inventors: Antong Chen, Dongqing Zhang, Ilknur Icke, Belma Dogdas, Sarayu Parimal
  • Publication number: 20190371436
    Abstract: Embodiments disclosed herein relate to a model for predicting the release profile of a controlled release device. The implant modeling system and models disclosed herein allow the accurate prediction of a release profile for a controlled release device based on features extracted from micro-resolution imagery. The models combine microstructural features that can be extracted at the XRCT resolution, including pore volume and connectivity, using erosion-dilation image analysis. This strategy allows prediction of release curves of the controlled release device using XRCT despite its resolution limitations.
    Type: Application
    Filed: March 4, 2019
    Publication date: December 5, 2019
    Inventors: Roberto Irizarry, Antong Chen, Daniel Skomski