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

Academic Article


  • 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 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. COMPARISON WITH EXISTING METHOD: 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.
  • Published In


  • Chronic stroke, Lesion-symptom mapping, Naïve Bayes classification, Segmentation, Supervised learning, T1-weighted MRI, Adult, Aged, Bayes Theorem, Brain, Brain Ischemia, Chronic Disease, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Reproducibility of Results, Stroke, Supervised Machine Learning, Young Adult
  • Digital Object Identifier (doi)

    Author List

  • Griffis JC; Allendorfer JB; Szaflarski JP
  • Start Page

  • 97
  • End Page

  • 108
  • Volume

  • 257