CRUISE: Cortical reconstruction using implicit surface evolution

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.

Authors

• Xiao Han
• Dzung L. Pham
• Duygu Tosun
• Maryam E. Rettmann
• Chenyang Xu School of Software Engineering, East China Normal University
• Jerry L. Prince

Keywords

• Magnetic resonance
• T1-weighted MR brain images
• Cerebral cortex
• Cortical reconstruction
• Human brain mapping