Abstract
MRI quantification of cranial nerves such as anterior visual pathway (AVP) in MRI is challenging due to their thin small size, structural variation along its path, and adjacent anatomic structures. Segmentation of pathologically abnormal optic nerve (e.g. optic nerve glioma) poses additional challenges due to changes in its shape at unpredictable locations. In this work, we propose a partitioned joint statistical shape model approach with sparse appearance learning for the segmentation of healthy and pathological AVP. Our main contributions are: (1) optimally partitioned statistical shape models for the AVP based on regional shape variations for greater local flexibility of statistical shape model; (2) refinement model to accommodate pathological regions as well as areas of subtle variation by training the model on-the-fly using the initial segmentation obtained in (1); (3) hierarchical deformable framework to incorporate scale information in partitioned shape and appearance models. Our method, entitled PAScAL (PArtitioned Shape and Appearance Learning), was evaluated on 21 MRI scans (15 healthy + 6 glioma cases) from pediatric patients (ages 2–17). The experimental results show that the proposed localized shape and sparse appearance-based learning approach significantly outperforms segmentation approaches in the analysis of pathological data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chan, J.: Optic Nerve Disorders. Springer, New York (2007)
Bekes, G., Máté, E., Nyúl, L.G., Kuba, A., Fidrich, M.: Geometrical model-based segmentation of the organs of sight on CT images. Med. Phys. 35(2), 735–743 (2008)
Noble, J.H., Dawant, B.M.: An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images. Med. Image Anal. 15(6), 877–884 (2011)
Yang, X., Cerrolaza, J., Duan, C., Zhao, Q., Murnick, J., Safdar, N., Avery, R., Linguraru, M.G.: Weighted partitioned active shape model for optic pathway segmentation in MRI. In: Linguraru, M.G., Laura, C.O., Shekhar, R., Wesarg, S., Ballester, M.Á.G., Drechsler, K., Sato, Y., Erdt, M. (eds.) CLIP 2014. LNCS, vol. 8680, pp. 109–117. Springer, Heidelberg (2017)
Mansoor, A., Bagci, U., Xu, Z., Foster, B., Olivier, K.N., Elinoff, J.M., Suffredini, A.F., Udupa, J.K., Mollura, D.J.: A generic approach to pathological lung segmentation. IEEE Trans. Med. Imaging 33(12), 2293–2310 (2014)
Cerrolaza, J.J., Reyes, M., Summers, R.M., González-Ballester, M., Linguraru, M.G.: Automatic multi-resolution shape modeling of multi-organ structures. Med. Image Anal. 25(1), 11–21 (2015)
Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Medical Imaging, pp. 236–248 (2001)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Linguraru, M.G., Sandberg, J.K., Jones, E.C., Petrick, N., Summers, R.M.: Assessing hepatomegaly: automated volumetric analysis of the liver. Acad. Radiol. 19(5), 588–598 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mansoor, A., Cerrolaza, J.J., Avery, R.A., Linguraru, M.G. (2016). Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2015. Lecture Notes in Computer Science(), vol 9401. Springer, Cham. https://doi.org/10.1007/978-3-319-31808-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-31808-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31807-3
Online ISBN: 978-3-319-31808-0
eBook Packages: Computer ScienceComputer Science (R0)