Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

Visual Information Processing Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UK.
NeuroImage (Impact Factor: 6.36). 03/2009; 46(3):726-38. DOI: 10.1016/j.neuroimage.2009.02.018
Source: PubMed

ABSTRACT Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

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Available from: Paul Aljabar, Sep 27, 2015
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    • "The propagated segmentations are then merged into a consensus label at each voxel in the target image. Voxelwise label conflicts can be resolved using either simple, unweighted approaches (Rohlfing et al., 2004a; Heckemann et al., 2006; Aljabar et al., 2009) or by weighting individual contributions locally based on the intensity information from the atlas and target images (Artaechevarria et al., 2009; Sabuncu et al., 2010). Alternative fusion strategies based on statistical optimisation have been proposed, with the most popular representative being STAPLE (Warfield et al., 2004) and its modifications (Asman and Landman, 2011, 2013; Landman et al., 2012; Cardoso et al., 2013a). "
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    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.
    Medical Image Analysis 02/2015; 21(1):40-58. DOI:10.1016/ · 3.65 Impact Factor
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    • "In addition, our healthy young adult atlas may not be the best template to achieve high transformation accuracy. More sophisticated approaches, such as multi-atlases (Aljabar, 2009; Heckemann, 2006; Iosifescu, 1997; Jia et al., 2012; Klein and Hirsch, 2005; Lao, 2004; Liu et al., 2004; Rohlfing et al., 2004; Wang, 2010; Warfield et al., 2004; Wu et al., 2007), may increase the accuracy in the future. For the image-vector conversion, ABA plays an important role in dimensional reduction. "
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    ABSTRACT: Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.
    Clinical neuroimaging 01/2015; 46. DOI:10.1016/j.nicl.2015.01.008 · 2.53 Impact Factor
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    • "Most of the MAS work applied to brain MRI data has focused on the segmentation of cortical and subcortical regions in structural images, typically acquired with T1-weighted MRI sequences. Many methods have been developed to parcellate the whole brain, segmenting it into a large number of regions (Aljabar et al., 2008; Babalola et al., 2009; Fonov et al., 2012; Han et al., 2009; Heckemann et al., 2010, 2011; Keihaninejad et al., 2010; Kotrotsou et al., 2014; Svarer et al., 2005; Wang et al., 2012), while other studies have focused on small sets or individual ROIs, such as the caudate nucleus (van Rikxoort et al., 2007b); the cerebellum (Park et al., 2014; Van Der Lijn et al., 2012; Weier et al., 2014); the amygdala (Hanson et al., 2012; Klein-Koerkamp et al., 2014); the corpus callosum (Ardekani et al., 2014); the subthalamic nucleus, red nucleus and substantia nigra (Xiao et al., 2014); the ventricles (Chou et al., 2008; Raamana et al., 2014); and, most notably, the hippocampus, which has attracted much attention due to its association with dementia and Alzheimer's disease (Akhondi-Asl et al., 2010; Bishop et al., 2010; Clerx et al., 2013; Hammers et al., 2007; Iglesias et al., 2010; Kim et al., 2012; Leung et al., 2010; Pipitone et al., 2014; Pluta et al., 2012; Raamana et al., 2014; van der Lijn et al., 2008; Van Der Lijn et al., 2012; Winston et al., 2013; Wolz et al., 2010b; Yushkevich et al., 2010). "
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    ABSTRACT: Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. Copyright © 2015 Elsevier B.V. All rights reserved.
    Medical image analysis 12/2014; 24(1). DOI:10.1016/ · 3.65 Impact Factor
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