Atlas-based identification of cortical sulci

Abstract

The identification of cortical sulci is of great importance. In neurosurgical procedures any target in the cranium can be accessed by following the corridors of the sulci and fissures. The fusion of functional and anatomical data also requires the identification of sulci. Several approaches have been proposed for segmentation of the cortical surface and identification of sulci and fissures. Most of them are bottom-up. They work satisfactorily provided that the sulci are well discernible on MRI images, limiting their use to some major sulci and fissures, such as the central sulcus, interhemispheric fissure, or Sylvian fissure. We propose a sulcal model based approach, overcoming some of the above limitations. The sulcal model is derived from two brain atlases: Co-Planar Stereotaxic Atlas of the Human Brain by Talairach- Tournoux (TT), and Atlas of Cerebral Sulci by Ono-Kubik-Abernathey (OKA). The OKA atlas contains 403 patterns for 55 sulci along with their incidence rates of interruptions, side branches, and connections. An electronic version of the OKA atlas was constructed, quantitatively enhanced by placing the sulcal patterns in a stereotactic space. The original patterns from the OKA atlas were digitized, converted into geometric representation, placed in the Talairach stereotactic space, preregistered with the TT atlas, and integrated with a multi- atlas, multi-dimensional neuroimaging system developed by our group. The registration of any atlas with the clinical data automatically registers all atlases with this data. This way the sulcal patterns can be superimposed on data, indicating approximate locations of sulci on images. The approach proposed here provides a simple and real-time registration of the sulcal patterns with clinical data, and an interactive identification and labeling of sulci. This approach assists rather the medical professional, instead of providing a complete automated extraction of a few, primary sulci with certain accuracy, where a higher accuracy usually demands a longer time of pattern extraction.