Abstract
We propose a pipeline for the characterization of facial and cochlear nerves in CT scans, a task specifically relevant for cochlear implant surgery planning. These structures are hard to locate in clinical CT scans due to their small size relative to the image resolution, the lack of contrast, and the proximity to other similar structures in this region. We define key landmarks around the facial and cochlear nerves and locate them using deep reinforcement learning with communicative multi-agents based on the C-MARL model. These landmarks are used as initialization for customized characterization methods. These include the automated direct measurement of the diameter of the cochlear nerve canal and extraction of the cochlear nerve cross-section followed by its segmentation using active contours. We also derive a path selection algorithm for optimal geodesic pathfinding selection based on Dijkstra’s algorithm for the characterization of the facial nerve. A total of 119 clinical CT images from preoperative patients have been used to develop this pipeline that produces accurate characterizations of these nerves in the cochlear region and provides reliable measurements for computer-aided diagnosis and surgery planning.
Keywords
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
Celik, O., Eskiizmir, G., Pabuscu, Y., Ulkumen, B., Toker, G.T.: The role of facial canal diameter in the pathogenesis and grade of bell’s palsy: a study by high resolution computed tomography. Brazilian J. Otorhinol. 83, 261–268 (2017)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)
Fatterpekar, G.M., Mukherji, S.K., Lin, Y., Alley, J.G., Stone, J.A., Castillo, M.: Normal canals at the fundus of the internal auditory canal: CT evaluation. J. Comput. Assist. Tomogr. 23, 776–780 (1999)
Fauser, J., et al.: Toward an automatic preoperative pipeline for image-guided temporal bone surgery. Int. J. Comput. Assist. Radiol. Surg. 14(6), 967–976 (2019)
Gare, B.M., Hudson, T., Rohani, S.A., Allen, D.G., Agrawal, S.K., Ladak, H.M.: Multi-atlas segmentation of the facial nerve from clinical CT for virtual reality simulators. Int. J. Comput. Assist. Radiol. Surg. 15, 259–267 (2020)
Ghesu, F.C., Georgescu, B., Grbic, S., Maier, A.K., Hornegger, J., Comaniciu, D.: Robust multi-scale anatomical landmark detection in incomplete 3D-CT data. Proc. MICCAI 2017, 194–202 (2017)
Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. Proc. MICCAI 2016, 229–237 (2016)
Ghesu, F.C., et al.: Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE Trans. Pattern Anal. Mach. Intell. 41, 176–189 (2019)
Hatch, J.L., et al.: Can preoperative CT scans be used to predict facial nerve stimulation following CI? Otol. Neurotol. 38, 1112–1117 (2017)
Leroy, G., Rueckert, D., Alansary, A.: Communicative reinforcement learning agents for landmark detection in brain images. In: Kia, S.M., et al. (eds.) MLCN/RNO-AI -2020. LNCS, vol. 12449, pp. 177–186. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66843-3_18
Li, Y., et al.: Fast multiple landmark localisation using a patch-based iterative network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 563–571. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_64
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Nikan, S., Osch, K.V., Bartling, M., Allen, D.G., Rohani, S.A., Connors, B., Agrawal, S.K., Ladak, H.M.: PWD-3DNet: a deep learning-based fully-automated segmentation of multiple structures on temporal bone CT scans. IEEE Trans. Image Process. 30, 739–753 (2021)
Noble, J.H., Warren, F.M., Labadie, R.F., Dawant, B.M.: Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values. Med. Phys. 35, 5375–5384 (2008)
Noothout, J.M.H., de Vos, B.D., Wolterink, J.M., Leiner, T., Isgum, I.: CNN-based landmark detection in cardiac CTA scans. CoRR abs/1804.04963 (2018). http://arxiv.org/abs/1804.04963
Oktay, O., et al.: Stratified decision forests for accurate anatomical landmark localization in cardiac images. IEEE Trans. Med. Imaging 36, 332–342 (2017)
Trier, P., Karsten Noe, M.S.S.: The visible ear simulator (2020). https://ves.alexandra.dk/
Powell, K.A., Kashikar, T., Hittle, B., Stredney, D., Kerwin, T., Wiet, G.J.: Atlas-based segmentation of temporal bone surface structures. Int. J. Comput. Assist. Radiol. Surg. 14, 1267–1273 (2019)
Powell, K.A., Liang, T., Hittle, B., Stredney, D., Kerwin, T., Wiet, G.J.: Atlas-based segmentation of temporal bone anatomy. Int. J. Comput. Assist. Radiol. Surg. 12, 1937–1944 (2017)
Vlontzos, A., Alansary, A., Kamnitsas, K., Rueckert, D., Kainz, B.: Multiple landmark detection using multi-agent reinforcement learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 262–270. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_29
Voormolen, E.H., et al.: Determination of a facial nerve safety zone for navigated temporal bone surgery. Neurosurgery 70, 50–60 (2012)
Waldman, S.D.: Chapter 9 - the vestibulocochlear nerve—cranial nerve viii. In: Waldman, S.D. (ed.) Pain Review, pp. 22–25. W.B. Saunders, Philadelphia (2009). http://www.sciencedirect.com/science/article/pii/B9781416058939000095
Watkins, C.J.C.H.: Learning from Delayed Rewards. Ph.D. thesis, King’s College, Cambridge, UK, May 1989
Xu, Z., et al.: Supervised action classifier: approaching landmark detection as image partitioning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 338–346. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_39
Yushkevich, P.A., Piven, J., Cody Hazlett, H., Gimpel Smith, R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)
Zhang, D., Liu, Y., Noble, J.H., Dawant, B.M.: Automatic localization of landmark sets in head CT images with regression forests for image registration initialization. Med. Imaging 2016 Image Process. 9784, 97841M (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
López Diez, P., Sundgaard, J.V., Patou, F., Margeta, J., Paulsen, R.R. (2021). Facial and Cochlear Nerves Characterization Using Deep Reinforcement Learning for Landmark Detection. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-87202-1_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87201-4
Online ISBN: 978-3-030-87202-1
eBook Packages: Computer ScienceComputer Science (R0)