BEaST: Brain extraction based on nonlocal segmentation technique

By Simon Fristed Eskildsen, Pierrick Coupé, Vladimir S Fonov, José Vicente Manjón, Kelvin K. Leung, Nicolas Guizard, Shafik N. Wassef, Lasse Riis Østergaard, D. Louis Collins. NeuroImage, 2012

485 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/dc536eabc68bef8033499bf046254fc7cfc172fb

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Citations in the corpus

volBrain: An Online MRI Brain Volumetry System (2015, methodology): “More recently, multi-atlas label fusion methods have been shown to be competitive (Leung et al., 2011; Eskildsen et al., 2012).”
Automated brain extraction of multisequence MRI using artificial neural networks (2019, methodology): “In contrast, average processing time of a single T1-w sequence with one of the six competing public brain extraction algorithms ranged from 3 s to 34.6 min (specifically, averages were 3 s for BSE, 17 s for BET, 1.4 min for ROBEX, 4.0 min for 3dSkullstrip, 10.7 min for BEaST, and 34.5 min for MONSTR”
A survey of MRI-based brain tumor segmentation methods (2014, background): “Skull stripping is the process of delineation and removal of non-cerebral tissue region such as skull, scalp, and meninges from the brain soft tissues [21] .”
Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates (2012, background): “, 2010], 80 [Collins and Pruessner, 2010], or even 160 [Eskildsen et al., 2011] subjects within a single template library.”
SynthStrip: skull-stripping for any brain image (2022, unknown): “Classical, or traditional, algorithms that remove non-brain image signal vary substantially in their implementation (Cox, 1996; Eskildsen et al., 2012; Iglesias et al., 2011; Roy et al., 2017; S´egonne et al., 2004; Shattuck et al., 2001; Smith, 2002).”
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping (2016, unknown): “This would be a feasible approach if the goal were to automatically train the CNN with larger amounts of training data, usually beneficial for deep learning architectures, and avoid tedious manual segmentations that can take up to 6–8 h for a 1 mm3 isotropic volume (Eskildsen et al., 2012).”
A review on automatic fetal and neonatal brain MRI segmentation (2017, unknown): “…https://www.nitrc.org/projects/neoseg Avants et al. (2011) https://github.com/stnava/ANTs Wang et al. (2011) http://www.nitrc.org/projects/ibeat Eskildsen et al. (2012) https://github.com/fristed/BEaST Shi et al. (2012) https://www.nitrc.org/projects/skulltoolkit Wang et al. (2012a)…”