Patents by Inventor Sarayu Parimal

Sarayu Parimal 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).

  • Multi-level convolutional LSTM model for the segmentation of MR images
    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
  • MULTI-LEVEL CONVOLUTIONAL LSTM MODEL FOR THE SEGMENTATION OF MR IMAGES
    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