Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease

Mahsa Dadar, Tharick A. Pascoal, Sarinporn Manitsirikul, Karen Misquitta, Vladimir S. Fonov, M. Carmela Tartaglia, John Breitner, Pedro Rosa-Neto, Owen T. Carmichael, Charles DeCarli, D. Louis Collins

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer's disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ICC=0.96 SI= 0.62 0.16 for ADC data set and ICC=0.78, SI=0.51 0.15 for PREVENT-AD data set. The proposed method was robust in the independent sample yielding SI=0.64\pm 0.17 with ICC=0.93 for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.

Original languageEnglish (US)
Article number7898360
Pages (from-to)1758-1768
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume36
Issue number8
DOIs
StatePublished - Aug 1 2017

Keywords

  • aging
  • Alzheimer's disease
  • segmentation
  • White matter hyperintensities

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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