Patents by Inventor Nathan S. Lay

Nathan S. Lay 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).

  • Detection of prostate cancer in multi-parametric MRI using random forest with instance weighting and MR prostate segmentation by deep learning with holistically-nested networks
    Patent number: 11200667
    Abstract: Disclosed prostate computer aided diagnosis (CAD) systems employ a Random Forest classifier to detect prostate cancer. System classify individual pixels inside the prostate as potential sites of cancer using a combination of spatial, intensity and texture features extracted from three sequences. The Random Forest training considers instance-level weighting for equal treatment of small and large cancerous lesions and small and large prostate backgrounds. Two other approaches are based on an AutoContext pipeline intended to make better use of sequence-specific patterns. Also disclosed are methods and systems for accurate automatic segmentation of the prostate in MRI. Methods can include both patch-based and holistic (image-to-image) deep learning methods for segmentation of the prostate. A patch-based convolutional network aims to refine the prostate contour given an initialization. A method for end- to-end prostate segmentation integrates holistically nested edge detection with fully convolutional networks.
    Type: Grant
    Filed: February 22, 2018
    Date of Patent: December 14, 2021
    Assignee: The United States of America, as represented by the Secretary, Department of Health and Human Services
    Inventors: Nathan S. Lay, Yohannes Tsehay, Ronald M. Summers, Baris Turkbey, Matthew Greer, Ruida Cheng, Holger Roth, Matthew J. McAuliffe, Sonia Gaur, Francesca Mertan, Peter Choyke
  • DETECTION OF PROSTATE CANCER IN MULTI-PARAMETRIC MRI USING RANDOM FOREST WITH INSTANCE WEIGHTING & MR PROSTATE SEGMENTATION BY DEEP LEARNING WITH HOLISTICALLY-NESTED NETWORKS
    Publication number: 20190370965
    Abstract: Disclosed prostate computer aided diagnosis (CAD) systems employ a Random Forest classifier to detect prostate cancer. System classify individual pixels inside the prostate as potential sites of cancer using a combination of spatial, intensity and texture features extracted from three sequences. The Random Forest training considers instance-level weighting for equal treatment of small and large cancerous lesions and small and large prostate backgrounds. Two other approaches are based on an AutoContext pipeline intended to make better use of sequence-specific patterns. Also disclosed are methods and systems for accurate automatic segmentation of the prostate in MRI. Methods can include both patch-based and holistic (image-to-image) deep learning methods for segmentation of the prostate. A patch-based convolutional network aims to refine the prostate contour given an initialization. A method for end- to-end prostate segmentation integrates holistically nested edge detection with fully convolutional networks.
    Type: Application
    Filed: February 22, 2018
    Publication date: December 5, 2019
    Applicant: The United States of America, as represented by the Secretary, Department of Health and Human Servic
    Inventors: Nathan S. Lay, Yohannes Tsehay, Ronald M. Summers, Baris Turkbey, Matthew Greer, Ruida Cheng, Holger Roth, Matthew J. McAuliffe, Sonia Gaur, Francesca Mertan, Peter Choyke