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:
Feb. 13, 2018
Filed:
Mar. 21, 2013
The Johns Hopkins University, Baltimore, MD (US);
National Institutes of Health, Rockville, MD (US);
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD (US);
Ciprian Crainiceanu, Baltimore, MD (US);
Arthur Jeffrey Goldsmith, New York, NY (US);
Dzung Pham, Alexandria, VA (US);
Daniel S. Reich, Washington, DC (US);
Navid Shiee, North Bethesda, MD (US);
Russell T. Shinohara, Philadelphia, PA (US);
Elizabeth M. Sweeney, Baltimore, MD (US);
The Johns Hopkins University, Baltimore, MD (US);
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD (US);
The United States of America, as Represented by the Secretary, Department of Health and Human Services, Washington, DC (US);
Abstract
The present invention, referred to as Oasis is Automated Statistical Inference for Segmentation (OASIS), is a fully automated and robust statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence. The OASIS model produces interpretable results in the form of regression coefficients that can be applied to imaging studies quickly and easily. OASIS uses intensity-normalized brain MRI volumes, enabling the model to be robust to changes in scanner and acquisition sequence. OASIS also adjusts for intensity inhomogeneities that preprocessing bias field correction procedures do not remove, using BLUR volumes. This allows for more accurate segmentation of brain areas that are highly distorted by inhomogeneities, such as the cerebellum. One of the most practical properties of OASIS is that the method is fully transparent, easy to implement, and simple to modify for new data sets.