Abstract
We propose a framework for the robust and fully-automatic
segmentation of magnetic resonance (MR) brain images
called "Multi-Atlas Label Propagation with
Expectation-Maximisation based refinement" (MALP-EM). The
presented approach is based on a robust registration
approach (MAPER), highly performant label fusion (joint
label fusion) and intensity-based label refinement using
EM. We further adapt this framework to be applicable for
the segmentation of brain images with gross changes in
anatomy. We propose to account for consistent
registration errors by relaxing anatomical priors
obtained by multi-atlas propagation and a weighting
scheme to locally combine anatomical atlas priors and
intensity-refined posterior probabilities. The method is
evaluated on a benchmark dataset used in a recent MICCAI
segmentation challenge. In this context we show that
MALP-EM is competitive for the segmentation of MR brain
scans of healthy adults when compared to state-of-the-art
automatic labelling techniques. To demonstrate the
versatility of the proposed approach, we employed MALP-EM
to segment 125 MR brain images into 134 regions from
subjects who had sustained traumatic brain injury (TBI).
We employ a protocol to assess segmentation quality if no
manual reference labels are available. Based on this
protocol, three independent, blinded raters confirmed on
13 MR brain scans with pathology that MALP-EM is superior
to established label fusion techniques. We visually
confirm the robustness of our segmentation approach on
the full cohort and investigate the potential of derived
symmetry-based imaging biomarkers that correlate with and
predict clinically relevant variables in TBI such as the
Marshall Classification (MC) or Glasgow Outcome Score
(GOS). Specifically, we show that we are able to stratify
TBI patients with favourable outcomes from non-favourable
outcomes with 64.7% accuracy using acute-phase MR images
and 66.8% accuracy using follow-up MR images.
Furthermore, we are able to differentiate subjects with
the presence of a mass lesion or midline shift from those
with diffuse brain injury with 76.0% accuracy. The
thalamus, putamen, pallidum and hippocampus are
particularly affected. Their involvement predicts TBI
disease progression.
Original language | English |
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Pages (from-to) | 40-58 |
Journal | Medical Image Analysis |
Volume | 21 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2015 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Traumatic brain injury
- Magnetic resonance imaging
- Multiatlas segmentation
- Brain image segmentation
- Expectation–maximisation