The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 14, 2021
Filed:
Feb. 22, 2018
The United States of America, As Represented BY the Secretary, Department of Health and Human Services, Bethesda, MD (US);
Nathan S. Lay, Bethesda, MD (US);
Yohannes Tsehay, Silver Spring, MD (US);
Ronald M. Summers, Potomac, MD (US);
Baris Turkbey, Rockville, MD (US);
Matthew Greer, Bethesda, MD (US);
Ruida Cheng, Bethesda, MD (US);
Holger Roth, Bavaria, DE;
Matthew J. McAuliffe, Bethesda, MD (US);
Sonia Gaur, Bethesda, MD (US);
Francesca Mertan, Bethesda, MD (US);
Peter Choyke, Rockville, MD (US);
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. HNNs automatically learn a hierarchical representation that improve prostate boundary detection.