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 entropy-based 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sultana, S., Blatt, J.E., Lee, Y., Ewend, M., Cetas, J.S., Costa, A., Audette, M.A.: Patient-specific cranial nerve identification using a discrete deformable contour model for skull base neurosurgery planning and simulation. In: Oyarzun Laura, C., et al. (eds.) CLIP 2015. LNCS, vol. 9401, pp. 36–44. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31808-0_5
Nain, D., Yezzi, A.J., Turk, G.: Vessel segmentation using a shape driven flow. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 51–59. Springer, Heidelberg (2004)
Unal, G., et al.: Shape-driven segmentation of the arterial wall in intravascular ultrasound images. IEEE Trans. Inf. Technol. Biomed. 12(3), 335–347 (2008)
Tejos, C., Irarrazaval, P., Cárdenas-Blanco, A.: Simplex mesh diffusion snakes: integrating 2D and 3D deformable models and statistical shape knowledge in a variational framework. Int. J. Comput. Vis. 85(1), 19–34 (2009)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Deschamps, T., Cohen, L.D.: Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Med. Image Anal. 5(4), 281–299 (2001)
Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32(2), 111–146 (1999)
Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape modeling and analysis with entropy-based particle systems. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 333–345. Springer, Heidelberg (2007)
Meyer, M.D., Georgel, P., Whitaker, R.T.: Robust particle systems for curvature dependent sampling of implicit surfaces. In: International Conference on Shape Modeling and Applications. IEEE (2005)
Gilles, B., Magnenat-Thalmann, N.: Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Image Anal. 14(3), 291–302 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Sultana, S. et al. (2016). Towards a Statistical Shape-Aware Deformable Contour Model for Cranial Nerve Identification. In: Shekhar, R., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2016. Lecture Notes in Computer Science(), vol 9958. Springer, Cham. https://doi.org/10.1007/978-3-319-46472-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-46472-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46471-8
Online ISBN: 978-3-319-46472-5
eBook Packages: Computer ScienceComputer Science (R0)