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 | |
| 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 | Germany |
| City | Munich |
| Period | 10/5/15 → 10/5/15 |
Fingerprint
Keywords
- Centerline
- Cranial nerves
- Minimal path
- Neurosurgery planning, personalized neurosurgery simulation
- Simplex
- Vesselness
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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
}
TY - GEN
T1 - Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation
AU - Sultana, Sharmin
AU - Blatt, Jason E.
AU - Lee, Yueh
AU - Ewend, Matthew
AU - Cetas, Justin
AU - Costa, Anthony
AU - Audette, Michel A.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Centerline
KW - Cranial nerves
KW - Minimal path
KW - Neurosurgery planning, personalized neurosurgery simulation
KW - Simplex
KW - Vesselness
UR - http://www.scopus.com/inward/record.url?scp=84962476471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962476471&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31808-0_5
DO - 10.1007/978-3-319-31808-0_5
M3 - Conference contribution
SN - 9783319318073
VL - 9401
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 44
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -