Johns Hopkins University

The United States of America, As Represented by the Secretary, Department of Health and Human Services

The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc.

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Daniel H Reich

Ciprian Crainiceanu

Russell T Shinohara

Arthur Jeffrey Goldsmith

Elizabeth M Sweeney

Navid Shiee

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.

Method of analyzing multi-sequence MRI data for analysing brain abnormalities in a subject

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Alexandria, VA, United States of America

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