A review of atlas-based segmentation for magnetic resonance brain images

By Mariano Cabezas, Arnau Oliver, Xavier Lladó, Jordi Freixenet, Meritxell Bach Cuadra. 2011

471 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “Algorithm and Data We describe a fully automated approach inspired by the human brain labelling method described in [90], and similar to multi-atlas label propagation methods [91]–[93].”
Computer Vision, Graphics, and Image Processing (2016, methodology): “Eigenfaces: Eigenfaces [13] are the set of eigenvectors obtained from the covariance matrix of training data.”
A survey of MRI-based brain tumor segmentation methods (2014, background): “A review of the automated approaches for atlas-based segmentation of magnetic resonance brain images was presented [83] .”
Brain templates and atlases (2012, background): “The creation and application of atlases for brain research are now widespread (see Cabezas et al., 2011 for a comprehensive review).”