Abstract
Automatic segmentation and identification of cortical sulci play an important role in the study of brain structure and function. In this work, a method is presented for the automatic segmentation of sulcal regions of cortex. Unlike previous methods that extract the sulcal spaces within the cortex, the proposed method extracts actual regions of the cortical surface that surround sulci. Sulcal regions are segmented from the medial surface as well as the lateral and inferior surfaces. The method first generates a depth map on the surface, computed by measuring the distance between the cortex and an outer “shrink-wrap” surface. Sulcal regions are then extracted using a hierarchical algorithm that alternates between thresholding and region growing operations. To visualize the buried regions of the segmented cortical surface, an efficient technique for mapping the surface to a sphere is proposed. Preliminary results are presented on the geometric analysis of sulcal regions for automated identification.
This work was partially supported by the NSF Presidential Faculty Fellow Award MIP93-50336, NIH R01NS37747 and a Whitaker Foundation graduate fellowship.
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Rettmann, M.E., Xu, C., Pham, D.L., Prince, J.L. (1999). Automated Segmentation of Sulcal Regions. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_17
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DOI: https://doi.org/10.1007/10704282_17
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