Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults

By G. Grabner, Andrew L. Janke, Marc M. Budge, David Smith, Jens C. Pruessner, D. Louis Collins. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2006

607 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/1dddfba077f5396ffc3d1da44f7426109fd55d99

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Local Temperature Scaling for Probability Calibration (2020, methodology): “For preprocessing, all images are first affinely registered to the ICBM MNI152 nonlinear atlas [15] using NiftyReg [40, 41, 50] and intensity normalized via histogram equalization.”
Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2016, methodology): “All images were resampled to an isotropic 1 mm 3 resolution, with dimensions 193 × 229 × 193 and aﬃnely registered (Studholme et al. (1999)) to MNI space using the atlas by Grabner et al. (2006).”
Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures (2015, positive): “) Register both cortical surfaces for each hemisphere non-linearly to a high resolution average surface template generated from the ICBM152 data set in order to establish inter-subject correspondence of the cortical points (Mazziotta et al., 1995; Lyttelton et al., 2007; Grabner et al., 2006).”
Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates (2012, methodology): “Most of these evaluations estimate how well nonlinear registration algorithms are able to recover anatomical differences between subjects and between a subject and a neuroanatomical template [Grabner et al., 2006; Holmes et al., 1998; Mazziotta et al., 2001a], but do little in the way of accounting ”
Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017, methodology): “All images used in our experiments are ﬁrst aﬃnely registered to the ICBM MNI152 nonlinear atlas [65] This regularizer is too weak to assure a diﬀeomorphic transformation based on the suﬃcient regularity conditions discussed in [13].”
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping (2016, methodology): “, 2002)was utilized to register the reference mask on the 1 mm MNI152 T1w average brain mask (Grabner et al., 2006).”
Unbiased Average Age-Appropriate Atlases for Pediatric Studies (2010, neutral): “…2001; Thompson and Toga, 2002; Essen and David, 2002; Thompson and Toga, 2002; Toga and Thompson, 2007; Essen and David, 2005; Seghers et al., 2004; Grabner et al., 2006); and (3) to function as a reference for a particular population group in order to study intra-and inter-group variability or…”
Brain templates and atlases (2012, background): “, a symmetric template to study hemispheric differences (Grabner et al., 2006), a single-gender template to study gender differences etc.”
Age-specific CT and MRI templates for spatial normalization (2012, neutral): “The majority of these templates are derived from healthy young adults, though specialized templates have been created for pediatric studies (Wilke et al., 2008) and healthy aging (Lemaître et al., 2005; Grabner et al., 2006).”