CRUISE: cortical reconstruction using implicit surface evolution

Neuroimage. 2004 Nov;23(3):997-1012. doi: 10.1016/j.neuroimage.2004.06.043.


Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms
  • Brain Mapping / methods*
  • Cerebral Cortex / anatomy & histology
  • Cerebral Cortex / physiology*
  • Fuzzy Logic
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Image Processing, Computer-Assisted / statistics & numerical data*
  • Magnetic Resonance Imaging / statistics & numerical data
  • Reproducibility of Results