Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

By Nicholas J. Tustison, P. Cook, Arno Klein, Gang Song, Sandhitsu R. Das, J. Duda, Benjamin M. Kandel, N. V. Strien, J. Stone, James C. Gee and 1 more. NeuroImage, 2014

676 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fe57487b827eb69801ce0ae41aaa45815a4672cf

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

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Citations in the corpus

Harmonization of cortical thickness measurements across scanners and sites (2017, methodology): “We note that the ANTs pipeline has been shown to perform exceptionally well for registration (Klein et al., 2009) as well as cortical thickness measurement in terms of minimal failure rate, higher repeatability, and improved predictive performance in thousands of images even compared to the state-of”
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks (2018, methodology): “The atlas of white matter, gray matter, and cerebrospinal fluid (CSF) in the ADNI T1-weighted images are generated using the SPM12 [1] segmentation and the ANTs SyN [19] non-linear registration algorithms.”
Mindboggling morphometry of human brains (2016, methodology): “io/ANTs/), which performs various image processing steps such as brain volume extraction [17,76], tissue-class segmentation [77], and registration-based labeling [16,18,76].”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Next, each subject's high-resolution structural image was processed using the ANTs Cortical Thickness Pipeline (Tustison et al., 2014).”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “sh script in advanced normalization tools (ANTs ; Avants et al., 2011a; Tustison et al., 2014).”
Reconstructing subject‐specific effect maps (2017, positive): “Furthermore, we also assumed the same for the cortical thickness measurements extracted from these images based on reliability properties of the Freesurfer based measurements (Jovicich et al., 2009; Tustison et al., 2014).”