title: Voxel-based Gaussian naïve 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.
  
  
  METHODS
  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 correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance.
  
  
  CONCLUSIONS
  Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations.
  
  
  CONCLUSIONS
  Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts.
authors.affiliation: Department of Psychology, The University of Alabama at Birmingham, United States. Electronic address: joegriff@uab.edu.
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Griffis
authors.affiliation: Department of Neurology, The University of Alabama at Birmingham, United States.
authors.forename: Jane B
authors.initials: JB
authors.lastname: Allendorfer
authors.affiliation: Department of Neurology, The University of Alabama at Birmingham, United States.
authors.forename: Jerzy P
authors.initials: JP
authors.lastname: Szaflarski
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000145:classification* / Q000473:pathology; D002908:Chronic Disease; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D020521:Stroke / Q000145:classification* / Q000473:pathology; D000069553:Supervised Machine Learning; D055815:Young Adult
keywords: Chronic stroke; Lesion-symptom mapping; Naïve Bayes classification; Segmentation; Supervised learning; T1-weighted MRI