Software Pipeline for Midsagittal Corpus Callosum Thickness Profile Processing: Automated Segmentation, Manual Editor, Thickness Profile Generator, Group-Wise Statistical Comparison and Results Display

Chris Adamson, Richard Beare, Mark Walterfang, Marc Seal

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

This paper presents a fully automated pipeline for thickness profile evaluation and analysis of the human corpus callosum (CC) in 3D structural T1-weighted magnetic resonance images. The pipeline performs the following sequence of steps: midsagittal plane extraction, CC segmentation algorithm, quality control tool, thickness profile generation, statistical analysis and results figure generator. The CC segmentation algorithm is a novel technique that is based on a template-based initialisation with refinement using mathematical morphology operations. The algorithm is demonstrated to have high segmentation accuracy when compared to manual segmentations on two large, publicly available datasets. Additionally, the resultant thickness profiles generated from the automated segmentations are shown to be highly correlated to those generated from the ground truth segmentations. The manual editing tool provides a user-friendly environment for correction of errors and quality control. Statistical analysis and a novel figure generator are provided to facilitate group-wise morphological analysis of the CC.

Original languageEnglish
Pages (from-to)595-614
Number of pages20
JournalNeuroInformatics
Volume12
Issue number4
DOIs
Publication statusPublished - Oct 2014
Externally publishedYes

Keywords

  • Corpus callosum
  • MRI
  • Software pipeline
  • Thickness

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