Towards a statistical shape-aware deformable contour model for cranial nerve identification

Sharmin Sultana; Praful Agrawal; Shireen Y. Elhabian; Ross T. Whitaker; Tanweer Rashid; Jason E. Blatt; Justin Cetas; Michel A. Audette

doi:10.1007/978-3-319-46472-5_9

Towards a statistical shape-aware deformable contour model for cranial nerve identification

Sharmin Sultana, Praful Agrawal, Shireen Y. Elhabian, Ross T. Whitaker, Tanweer Rashid, Jason E. Blatt, Justin Cetas, Michel A. Audette

Neurosurgery

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

3 Scopus citations

Abstract

This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropybased energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.

Original language	English (US)
Title of host publication	Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings
Publisher	Springer Verlag
Pages	68-76
Number of pages	9
Volume	9958 LNCS
ISBN (Print)	9783319464718
DOIs	https://doi.org/10.1007/978-3-319-46472-5_9
State	Published - 2016
Event	5th International Workshop on Clinical Image-Based Procedures, CLIP 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: Oct 17 2016 → Oct 17 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9958 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	5th International Workshop on Clinical Image-Based Procedures, CLIP 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country	Greece
City	Athens
Period	10/17/16 → 10/17/16

Keywords

Centerline
Contour model
Cranial nerves
Patient-specific segmentation
Statistical Shape Model

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-46472-5_9

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Cite this

Sultana, S., Agrawal, P., Elhabian, S. Y., Whitaker, R. T., Rashid, T., Blatt, J. E., Cetas, J., & Audette, M. A. (2016). Towards a statistical shape-aware deformable contour model for cranial nerve identification. In Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings (Vol. 9958 LNCS, pp. 68-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9958 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46472-5_9

Towards a statistical shape-aware deformable contour model for cranial nerve identification. / Sultana, Sharmin; Agrawal, Praful; Elhabian, Shireen Y.; Whitaker, Ross T.; Rashid, Tanweer; Blatt, Jason E.; Cetas, Justin; Audette, Michel A.

Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9958 LNCS Springer Verlag, 2016. p. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9958 LNCS).

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

Sultana, S, Agrawal, P, Elhabian, SY, Whitaker, RT, Rashid, T, Blatt, JE, Cetas, J & Audette, MA 2016, Towards a statistical shape-aware deformable contour model for cranial nerve identification. in Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings. vol. 9958 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9958 LNCS, Springer Verlag, pp. 68-76, 5th International Workshop on Clinical Image-Based Procedures, CLIP 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 10/17/16. https://doi.org/10.1007/978-3-319-46472-5_9

Sultana S, Agrawal P, Elhabian SY, Whitaker RT, Rashid T, Blatt JE et al. Towards a statistical shape-aware deformable contour model for cranial nerve identification. In Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9958 LNCS. Springer Verlag. 2016. p. 68-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46472-5_9

Sultana, Sharmin ; Agrawal, Praful ; Elhabian, Shireen Y. ; Whitaker, Ross T. ; Rashid, Tanweer ; Blatt, Jason E. ; Cetas, Justin ; Audette, Michel A. / Towards a statistical shape-aware deformable contour model for cranial nerve identification. Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9958 LNCS Springer Verlag, 2016. pp. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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