FreeSurfer

Freesurfer. FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.


References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Dassi, Franco; Kroos, Julia M.; Gerardo-Giorda, L.; Perotto, Simona: A denoising tool for the reconstruction of cortical geometries from MRI (2022)
  2. Jensen, Henrik G.; Lauze, François; Darkner, Sune: Information-theoretic registration with explicit reorientation of diffusion-weighted images (2022)
  3. Zhang, Hao; Guilleminot, Johann; Gomez, Luis J.: Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometries (2021)
  4. Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
  5. Conte, Martina; Gerardo-Giorda, Luca; Groppi, Maria: Glioma invasion and its interplay with nervous tissue and therapy: a multiscale model (2020)
  6. Mejia, Amanda F.; Yue, Yu (Ryan); Bolin, David; Lindgren, Finn; Lindquist, Martin A.: A Bayesian general linear modeling approach to cortical surface fMRI data analysis (2020)
  7. Pinheiro, G. R.; Carmo, D. S.; Yasuda, C.; Lotufo, R. A.; Rittner, L.: Convolutional neural network on DTI data for sub-cortical brain structure segmentation (2020)
  8. Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
  9. Vandekar, Simon N.; Reiss, Philip T.; Shinohara, Russell T.: Interpretable high-dimensional inference via score projection with an application in neuroimaging (2019)
  10. Anh Phong Tran, Qianqian Fang: Fast and high-quality tetrahedral mesh generation from neuroanatomical scans (2017) arXiv
  11. Gerardo-Giorda, Luca; Kroos, Julia M.: A computational multiscale model of cortical spreading depression propagation (2017)
  12. Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German: Centered kernel alignment enhancing neural network pretraining for MRI-based dementia diagnosis (2016)
  13. Csaba Kerepesi, Balazs Szalkai, Balint Varga, Vince Grolmusz: The braingraph.org Database of High Resolution Structural Connectomes and the Brain Graph Tools (2016) arXiv
  14. Liu, Zhuqing; Berrocal, Veronica J.; Bartsch, Andreas J.; Johnson, Timothy D.: Pre-surgical fMRI data analysis using a spatially adaptive conditionally autoregressive model (2016)
  15. John Muschelli, Elizabeth Sweeney, Martin Lindquist, Ciprian Crainiceanu: fslr: Connecting the FSL Software with R (2015) not zbMATH
  16. Bruce Fischl: FreeSurfer (2012) not zbMATH