Journal
MEDICAL IMAGE ANALYSIS
Volume 21, Issue 1, Pages 40-58Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2014.12.003
Keywords
Traumatic brain injury; Magnetic resonance imaging; Multi-atlas segmentation; Brain image segmentation; Expectation-maximisation
Categories
Funding
- 7th Framework Programme by the European Commission
- National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust
- Imperial College London
- Department of Health via the NIHR comprehensive BRC award
- King's College London
- Kings College Hospital NHS Foundation Trust
- Medical Research Council (UK) Program Grant
- UK National Institute of Health Research Biomedical Research Centre at Cambridge
- Technology Platform - UK Department of Health
- EPSRC Pathways
- Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship
- NIHR Senior Investigator Award
- [G9439390 ID 658831]
- Academy of Medical Sciences (AMS) [AMS-CSF4-Newcombe] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/I000445/1, EP/K503733/1] Funding Source: researchfish
- Medical Research Council [G1000183B, G0001354B, G9439390, G0001354] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10090] Funding Source: researchfish
- EPSRC [EP/I000445/1] Funding Source: UKRI
- MRC [G9439390] Funding Source: UKRI
Ask authors/readers for more resources
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. (C) 2014 The Authors. Published by Elsevier B.V.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available