Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation

Sharmin Sultana; Jason E. Blatt; Yueh Lee; Matthew Ewend; Justin Cetas; Anthony Costa; Michel A. Audette

doi:10.1007/978-3-319-31808-0_5

Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation

Sharmin Sultana, Jason E. Blatt, Yueh Lee, Matthew Ewend, Justin Cetas, Anthony Costa, Michel A. Audette

Neurosurgery

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Pages	36-44
Number of pages	9
Volume	9401
ISBN (Print)	9783319318073
DOIs	https://doi.org/10.1007/978-3-319-31808-0_5
State	Published - 2016
Event	4th International Workshop on Clinical Image-Based Procedures, CLIP 2015 and Held in 18th International Conference on Medical Image Computing and Computer-Assisted Interventions, MICCAI MICCAI 2015 - Munich, Germany Duration: Oct 5 2015 → Oct 5 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9401
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	4th International Workshop on Clinical Image-Based Procedures, CLIP 2015 and Held in 18th International Conference on Medical Image Computing and Computer-Assisted Interventions, MICCAI MICCAI 2015
Country/Territory	Germany
City	Munich
Period	10/5/15 → 10/5/15

Keywords

Centerline
Cranial nerves
Minimal path
Neurosurgery planning, personalized neurosurgery simulation
Simplex
Vesselness

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-319-31808-0_5

Cite this

Sultana, S., Blatt, J. E., Lee, Y., Ewend, M., Cetas, J., Costa, A., & Audette, M. A. (2016). Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9401, pp. 36-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9401). Springer Verlag. https://doi.org/10.1007/978-3-319-31808-0_5

Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. / Sultana, Sharmin; Blatt, Jason E.; Lee, Yueh; Ewend, Matthew; Cetas, Justin; Costa, Anthony; Audette, Michel A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9401 Springer Verlag, 2016. p. 36-44 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9401).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sultana, S, Blatt, JE, Lee, Y, Ewend, M, Cetas, J, Costa, A & Audette, MA 2016, Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9401, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9401, Springer Verlag, pp. 36-44, 4th International Workshop on Clinical Image-Based Procedures, CLIP 2015 and Held in 18th International Conference on Medical Image Computing and Computer-Assisted Interventions, MICCAI MICCAI 2015, Munich, Germany, 10/5/15. https://doi.org/10.1007/978-3-319-31808-0_5

Sultana S, Blatt JE, Lee Y, Ewend M, Cetas J, Costa A et al. Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9401. Springer Verlag. 2016. p. 36-44. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-31808-0_5

Sultana, Sharmin ; Blatt, Jason E. ; Lee, Yueh ; Ewend, Matthew ; Cetas, Justin ; Costa, Anthony ; Audette, Michel A. / Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9401 Springer Verlag, 2016. pp. 36-44 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation

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