Voxel-based Gaussian naive Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

ABSTRACT:

Background

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding.

New method

We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions.

Results

Our method c

SUBMITTER: Griffis JC 

PROVIDER: S-EPMC4662880 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature