Warping an atlas derived from serial histology to 5 high-resolution MRIs

Sci Data. 2018 Jun 19;5:180107. doi: 10.1038/sdata.2018.107.

Abstract

Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

Publication types

  • Dataset

MeSH terms

  • Atlases as Topic
  • Brain / anatomy & histology*
  • Brain / diagnostic imaging*
  • Humans
  • Magnetic Resonance Imaging

Associated data

  • figshare/10.6084/m9.figshare.c.4052768