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

US11200667B2 · kind B2 · utility

Assignee

• The United States of America, as represented by the Secretary, Department of Health and Human Services · US

Inventors

• Nathan Lay · Plainsboro, US
• Yohannes Tsehay · Silver Spring, US
• Ronald M. Summers · Potomac, US
• Baris Turkbey · Rockville, US
• Matthew Greer · Bethesda, US
• Ruida Cheng · Bethesda, US
• Holger Reinhard Roth · Rockville, US
• Matthew J. McAuliffe · Bethesda, US
• Sonia Gaur · Bethesda, US
• Francesca Mertan · Bethesda, US
• Peter Choyke · Rockville, US

Key dates

• Filing date: Feb 22, 2018
• Grant date: Dec 14, 2021
• Expiry date: May 11, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30096
• WIPO field: Medical technology
• WIPO sector: Instruments

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.