Abstract
We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
What is a Cochlear Implant. https://www.fda.gov/medical-devices/cochlear-implants/what-cochlear-implant. Accessed 17 Nov 2020
Image-guided Cochlear Implant Programming (IGCIP). https://clinicaltrials.gov/ct2/show/NCT03306082. Accessed 17 Nov 2020
Noble, J.H., et al.: Automatic segmentation of intracochlear anatomy in conventional CT. IEEE Trans. Biomed. Eng. 58(9), 2625–2632 (2011)
Wang, J., et al.: Metal artifact reduction for the segmentation of the intra cochlear anatomy in CT images of the ear with 3D-conditional GANs. Med. Image Anal. 58, 101553 (2019)
Wang, J., et al.: Conditional generative gdversarial networks for metal artifact reduction in CT images of the ear. In: Frangi, A., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science, vol. 11070, pp. 1–3. Springer, Cham (2018)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv:1411.1784 (2014)
Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1125–1134 (2017)
Pelosi, S., et al.: Analysis of intersubject variations in intracochlear and middle ear surface anatomy for cochlear implantation. Otol. Neurotol. 34(9), 1675–1680 (2013)
Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Trans. Med. Imaging 20(7), 568–582 (2001)
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)
Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1–13 (2018)
Kim, B., Kim, J., Lee, J.-G., Kim, D.H., Park, S.H., Ye, J.C.: Unsupervised deformable image registration using cycle-consistent CNN. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 166–174. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_19
Rueckert, D., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 1070–1074 (2018)
Ghavami, N., et al.: Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks. In: Fei, B., Webster III, R.J. (eds.) Proceedings Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576, pp. 1057603 (2018)
Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)
Acknowledgments
This work has been supported by NIH grants R01DC014037 and R01DC014462 and by the Advanced Computing Center for Research and Education (ACCRE) of Vanderbilt University. The content is solely the responsibility of the authors and does not necessarily represent the official views of these institutes.
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
Wang, J., Su, D., Fan, Y., Chakravorti, S., Noble, J.H., Dawant, B.M. (2021). Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-87202-1_2
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)