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Efficient Multi-Atlas Registration using an Intermediate Template Image.

Blake E Dewey1, Aaron Carass1,2, Ari M Blitz3

  • 1Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.

Proceedings of Spie--The International Society for Optical Engineering
|September 26, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a faster method for human brain labeling using multi-atlas label fusion. A novel registration technique significantly reduces computation time with minimal impact on label accuracy, even for challenging cases like hydrocephalus.

Keywords:
deformable registrationmulti-atlas label fusionnormal-pressure hydrocephalus

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Multi-atlas label fusion is a precise method for human brain labeling but is computationally intensive.
  • Registration through an intermediate template image can reduce processing time by storing deformations.
  • However, this through-template registration can negatively impact label accuracy.

Purpose of the Study:

  • To investigate the impact of through-template registration on multi-atlas label fusion accuracy.
  • To propose and evaluate a novel registration technique to mitigate accuracy loss.
  • To assess the trade-off between computation time and label accuracy.

Main Methods:

  • Implemented a novel registration technique to correct for distortions introduced by through-template registration.
  • Evaluated the method on healthy human brain datasets and datasets from patients with normal-pressure hydrocephalus.
  • Quantified label accuracy using Dice similarity and measured computation time.

Main Results:

  • The proposed correction method recovered almost complete Dice similarity compared to simple through-template registration.
  • Computation time was reduced by 3-4 times with minimal impact on overall label accuracy.
  • Substantially improved ventricular labeling accuracy in normal-pressure hydrocephalus cases.

Conclusions:

  • The novel registration technique effectively counteracts the negative effects of through-template registration in multi-atlas label fusion.
  • Computation time can be significantly decreased while maintaining or improving label accuracy.
  • The method shows particular benefit for labeling anatomically challenging brains, such as those affected by hydrocephalus.