Patient-Specific Cranial Nerve Identification Using a Discrete Deformable Contour Model for Skull Base Neurosurgery Planning and Simulation

• Published in: Clinical Image-Based Procedures. Translational Research in Medical Imaging Vol. 9401; pp. 36 - 44
• Main Authors: Sultana, Sharmin, Blatt, Jason E., Lee, Yueh, Ewend, Matthew, Cetas, Justin S, Costa, Anthony, Audette, Michel A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Centerline; Cranial nerves; Image processing; Minimal path; Neurosurgery planning, personalized neurosurgery simulation; Simplex; Vesselness
• ISBN: 3319318071; 9783319318073
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-31808-0_5

In this paper, we present a minimally supervised method for the identification of the intra-cranial portion of cranial nerves, using a novel, discrete 1-Simplex 3D active contour model. The clinical applications include planning and personalized simulation of skull base neurosurgery. The centerline of a cranial nerve is initialized from two user-supplied end points by computing a Minimal Path. The 1-Simplex is a Newtonian model for vertex motion, where every non-endpoint vertex has 2-connectivity with neighboring vertices, with which it is linked by edges. The segmentation behavior of the model is governed by the equilibrium between internal and external forces. The external forces include an image force that favors a centered path within high-vesselness points. The method is validated quantitatively using synthetic and real MRI datasets.