Abstract
The ventro-intermediate nucleus (Vim), as the others thalamic subparts, cannot be directly visualized by current standard magnetic resonance imaging (MRI), in daily clinical practice. Hence, for treatment of tremor in functional neurosurgery, where the commonly used target is the Vim, the targeting procedure is done indirectly. We present a novel direct automated segmentation of the Vim using only subject-related MRI information, specifically, diffusion MRI at 3T and susceptibility weighted images (SWI) acquired at 7T. With a state-of-the-art method based on local diffusion MR properties for automated subdivision of the thalamus, we first restrain the region of interest to the group of motor-related nuclei. Then, this thalamic part is further subdivided, in graph parcellation manner, using the intensity-related features provided by SWI together with prior knowledge of the Vim localization inside the motor thalamic segment. Our framework was tested in four healthy elderly subjects, for eight thalami in total, and the results were evaluated by an experienced neurosurgeon, showing the ability to directly detect the Vim area. The qualitative inspection indicated that the proposed method outperforms standard multi-atlas based techniques.
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
Lipsman, N., Schwartz, M.L., Huang, Y., Lee, L., Sankar, T., Chapman, M., Hynynen, K., Lozano, A.M.: MR-guided focused ultrasound thalamotomy for essential tremor: a proof-of-concept study. Lancet Neurol. 12, 462–468 (2013)
Witjas, T., Carron, R., Krack, P., Eusebio, A., Vaugoyeau, M., Hariz, M., Azulay, J.P., Regis, J.: A prospective single-blind study of Gamma Knife thalamotomy for tremor. Neurology 85, 1562–1568 (2015)
Kondziolka, D., Ong, J.G., Lee, J.Y., Moore, R.Y., Flickinger, J.C., Lunsford, L.D.: Gamma Knife thalamotomy for essential tremor. J. Neurosurg. 108, 111–117 (2008)
Battistella, G., Najdenovska, E., Maeder, P., Ghazaleh, N., Daducci, A., Thiran, J.P., Jacquemont, S., Tuleasca, C., Levivier, M., Bach Cuadra, M., Fornari, E.: Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties. Brain Struct. Funct. (2016)
Behrens, T.E., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003)
Mang, S.C., Busza, A., Reiterer, S., Grodd, W., Klose, A.U.: Thalamus segmentation based on the local diffusion direction: a group study. Magn. Reson. Med. 67, 118–126 (2012). Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine
Wiegell, M.R., Tuch, D.S., Larsson, H.B., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. NeuroImage 19, 391–401 (2003)
Ziyan, U., Tuch, D., Westin, C.-F.: Segmentation of thalamic nuclei from DTI using spectral clustering. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 807–814. Springer, Heidelberg (2006). doi:10.1007/11866763_99
Abosch, A., Yacoub, E., Ugurbil, K., Harel, N.: An assessment of current brain targets for deep brain stimulation surgery with susceptibility-weighted imaging at 7 tesla. Neurosurgery 67, 1745–1756 (2010). Discussion 1756
Fonov, V.S., Evans, A.C., McKinstry, R.C., Almli, C.R., Collins, D.L.: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47(Supplement 1), S102 (2009)
Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22, 53–66 (2012)
Veraart, J., Novikov, D.S., Christiaens, D., Ades-Aron, B., Sijbers, J., Fieremans, E.: Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016)
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(Suppl 1), S208–S219 (2004)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001)
Leemans, A., Jones, D.K.: The B-Matrix must be rotated when correcting for subject motion in DTI Data. Magn. Reson. Med. 61, 1336–1349 (2009)
Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016)
Schaltenbrand, G., Wahren, W.: Atlas for Stereotaxy of the Human Brain. Year Book Medical Publishers (1977)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y.J., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19, 143–150 (2000)
Shi, J.B., Malik, J.: Normalized cuts and image segmentation. In: Proceedings of CVPR IEEE, pp. 731–737 (1997)
Wang, H., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation. Front. Neuroinform. 7, 27 (2013)
Tuite, P.J., Dagher, A.: Magnetic Resonance Imaging in Movement Disorders: A Guide for Clinicians and Scientists. Cambridge University Press, Cambridge (2013)
Acknowledgements
The work was supported by the Swiss National Science Foundation (SNSF-205321-157040) and by the Centre d’Imagerie BioMédicale (CIBM) of the University of Lausanne (UNIL), the Swiss Federal Institute of Technology Lausanne (EPFL), the University of Geneva (UniGe), the Centre Hospitalier Universitaire Vaudois (CHUV), the Hôpitaux Universitaires de Genève (HUG), and the Leenaards and Jeantet Foundations.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Najdenovska, E. et al. (2017). Towards an Automated Segmentation of the Ventro-Intermediate Thalamic Nucleus. In: Cardoso, M., et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-67543-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67542-8
Online ISBN: 978-3-319-67543-5
eBook Packages: Computer ScienceComputer Science (R0)