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Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration.

Jordan Bai1, Thi Lan Huong Trinh, Kai-Hsiang Chuang

  • 1Department of Bioengineering, National University of Singapore, Singapore.

Magnetic Resonance Imaging
|April 3, 2012
PubMed
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Image registration significantly improves mouse brain segmentation accuracy more than model complexity. Large deformation diffeomorphic metric mapping (LDDMM) with multiple atlases offers the best results for automated mouse brain segmentation.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Atlas-based segmentation is crucial for analyzing brain structures.
  • Limited research exists for automated mouse brain segmentation compared to human brain studies.
  • Understanding the impact of image registration and model complexity is vital for improving mouse brain segmentation.

Purpose of the Study:

  • To investigate the roles of image registration algorithms and segmentation model complexity in mouse brain segmentation.
  • To compare the performance of different registration methods (affine, B-spline FFD, Demons, LDDMM) and segmentation models (single atlas, multiatlas, STAPLE, MRF).
  • To determine the optimal strategy for accurate automated mouse brain segmentation.

Main Methods:

  • Employed four segmentation models: single atlas, multiatlas, STAPLE, and MRF.

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  • Utilized four image registration algorithms: affine, B-spline FFD, Demons, and LDDMM.
  • Delineated 19 brain structures from in vivo magnetic resonance microscopy images and validated against manual segmentation.

    • Main Results:

    • LDDMM registration consistently outperformed Demons, FFD, and affine registration across all segmentation models.
    • Increasing segmentation model complexity (e.g., from single atlas to multiatlas) significantly improved accuracy.
    • Nonlinear registrations (FFD, Demons, LDDMM) with multiatlas methods showed comparable or superior performance to STAPLE and MRF, with LDDMM being the most effective.

    Conclusions:

    • Image registration is a more critical factor than segmentation model complexity for accurate automated mouse brain segmentation.
    • The combination of multiple atlases with LDDMM registration provides the highest segmentation accuracy for the mouse brain among the tested methods.
    • Findings guide the development of more precise tools for mouse brain analysis in research.