Fast and robust multi-atlas segmentation of brain magnetic resonance images

By Jyrki Lötjönen, Robin Wolz, Juha Koikkalainen, L. Thurfjell, G. Waldemar, Hilkka Soininen, Daniel Rueckert. NeuroImage, 2010

415 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/6fb13081e034b919f52b30a1b953328349c0c877

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

volBrain: An Online MRI Brain Volumetry System (2015, methodology): “We used a tissue-derived approach to force mean intensities of WM, GM, and CSF to be as similar as possible across subjects of the library in a similar manner than Lötjönen et al. (2010).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “This behaviour can also be observed in Table 3, where the best Dice values for each structure are obtained from multi-atlas approaches [65–67], closely followed by statistical frameworks [55,78,108].”
A supervised patch-based approach for human brain labeling (2011, neutral): “The labels are then mapped and fused using classification rules to produce a new label map of the input image [30], [15], [3], [2], [16], [8], [33], [35], [26], [38], [34], [25], [19].”
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, methodology): “Baseline hippocampal volumes were measured using an approach based on fast and robust multi-atlas segmentation [35,36].”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, background): “Several studies (Bai et al., 2012; Lotjonen et al., 2009; L¨otj¨onen et al., 2010; Sj¨oberg and Ahnesj¨o, 2013) have conducted empirical comparisons of the impact of diﬀerent registration algorithms on MAS performance in diﬀerent applications.”
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, neutral): “…of the MAS framework, with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al.,…”
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation (2011, methodology): “Lotjonen et al. (2010) proposed two intensity-based models to improve label fusion: an extension of the graph-cut-based method described by van der Lijn et al. (2008) and an expectation-maximization (EM) approach.”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “As described in the previous section, based on [1], [42], we implemented strategy 2 by computing the sum of squared differences between the intensity values of the test subject and each training subject in a predefined mask after affine”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, neutral): “For example, multi-atlas label fusion (MALF) made use of multiple reference atlases and achieved good performance in brain segmentation tasks (Aljabar et al., 2009; Lötjönen et al., 2010; Wang et al., 2013; Heckemann et al., 2006).”
3D fully convolutional networks for subcortical segmentation in MRI: A large‐scale study (2016, methodology): “We can divide prior-art methods into four main categories: atlas-based methods [10, 11], statistical models [12, 13], deformable models [14] and machine learning based classiﬁers [15, 16].”
Multi-Atlas Segmentation with Joint Label Fusion (2012, methodology): “This method requires higher computational costs but, as extensive empirical studies have verified in the recent literature, e.g., [16], [3], [22], it is more accurate than single atlas-based segmentation.”