Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.
"The multi-atlas labeling method employs several different atlases to cover a variety of MR data characteristics. Utilization of multiple atlases in a segmentation method takes account for image profile differences and large intersubject anatomical variation that naturally occurs in the human brain (Cabezas et al., 2011). There is also increasing evidence that multi-atlas labeling improves segmentation accuracy in several studies (Wang et al., 2012; Chakravarty et al., 2013; Sjoberg and Ahnesjo, 2013), and consequently, this approach is rapidly gaining popularity (Sabuncu et al., 2010; Zhang et al., 2011a; Jimenez del Toro and Muller, 2014). "
[Show abstract][Hide abstract] ABSTRACT: Multicenter longitudinal neuroimaging has great potential to provide efficient and consistent biomarkers for research of neurodegenerative diseases and aging. In rare disease studies it is of primary importance to have a reliable tool that performs consistently for data from many different collection sites to increase study power. A multi-atlas labeling algorithm is a powerful brain image segmentation approach that is becoming increasingly popular in image processing. The present study examined the performance of multi-atlas labeling tools for subcortical identification using two types of in-vivo image database: Traveling Human Phantom (THP) and PREDICT-HD. We compared the accuracy (Dice Similarity Coefficient; DSC and intraclass correlation; ICC), multicenter reliability (Coefficient of Variance; CV), and longitudinal reliability (volume trajectory smoothness and Akaike Information Criterion; AIC) of three automated segmentation approaches: two multi-atlas labeling tools, MABMIS and MALF, and a machine-learning-based tool, BRAINSCut. In general, MALF showed the best performance (higher DSC, ICC, lower CV, AIC, and smoother trajectory) with a couple of exceptions. First, the results of accumben, where BRAINSCut showed higher reliability, were still premature to discuss their reliability levels since their validity is still in doubt (DSC < 0.7, ICC < 0.7). For caudate, BRAINSCut presented slightly better accuracy while MALF showed significantly smoother longitudinal trajectory. We discuss advantages and limitations of these performance variations and conclude that improved segmentation quality can be achieved using multi-atlas labeling methods. While multi-atlas labeling methods are likely to help improve overall segmentation quality, caution has to be taken when one chooses an approach, as our results suggest that segmentation outcome can vary depending on research interest.
Frontiers in Neuroscience 07/2015; 9:242. DOI:10.3389/fnins.2015.00242 · 3.66 Impact Factor
"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). A more detailed overview of atlas-based methods is provided by Cabezas et al. (2011). A particular successful strategy called joint label fusion was recently proposed by Wang et al. (2013). "
[Show abstract][Hide abstract] 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/j.media.2014.12.003 · 3.65 Impact Factor
"A priori information is usually introduced in two different ways. On the one hand, a common strategy consists in integrating tissue priors to each voxel of the image by registering a template (called atlas) with each testing image (Cabezas et al., 2011). Those atlases define a soft segmentation of the brain providing a probability for each voxel to belong to a certain structure or tissue. "
[Show abstract][Hide abstract] ABSTRACT: Background: Automatic multiple sclerosis lesion segmentation is a challenging task. An extensive analysis of the most recent techniques indicates an improvement of the results obtained when using prior knowledge and contextual information. New method: We present BOOST, a knowledge-based approach to automatically segment multiple sclerosis lesions through a voxel by voxel classification. We used the Gentleboost classifier and a set of features, including contextual features, registered atlas probability maps and an outlier map. Results: Results are computed on a set of 45 cases from three different hospitals (15 of each), obtaining a moderate agreement between the manual annotations and the automatically segmented results. Comparison with existing method(s): We quantitatively compared our results with three public state-of-the-art approaches obtaining competitive results and a better overlap with manual annotations. Our approach tends to better segment those cases with high lesion load, while cases with small lesion load are more difficult to accurately segment. Conclusions: We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.
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