Abstract
Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain µCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.
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References
Abdollahi, A., Pradhan, B., Alamri, A.: Vnet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data. IEEE Access 8, 179424–179436 (2020)
Cakir, A., Dawant, B.M., Noble, J.H.: Development of a \(\upmu \)CT-based patient-specific model of the electrically stimulated cochlea. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 773–780. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_88
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Fan, Y., Zhang, D., Wang, J., Noble, J.H., Dawant, B.M.: Combining model-and deep-learning-based methods for the accurate and robust segmentation of the intra-cochlear anatomy in clinical head ct images. In: Medical Imaging 2020: Image Processing, vol. 11313, pp. 315–322. SPIE (2020)
Frijns, J.H., Briaire, J.J., Grote, J.J.: The importance of human cochlear anatomy for the results of modiolus-hugging multichannel cochlear implants. Otology Neurotol. 22(3), 340–349 (2001)
Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z., Cheng, M.M., Hu, S.M.: Segnext: rethinking convolutional attention design for semantic segmentation. arXiv preprint arXiv:2209.08575 (2022)
Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019)
Iglesias, J.E., et al.: Joint super-resolution and synthesis of 1 mm isotropic mp-rage volumes from clinical MRI exams with scans of different orientation, resolution and contrast. Neuroimage 237, 118206 (2021)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Kalkman, R.K., Briaire, J.J., Frijns, J.H.: Current focussing in cochlear implants: an analysis of neural recruitment in a computational model. Hear. Res. 322, 89–98 (2015)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
Li, Y., Sixou, B., Peyrin, F.: A review of the deep learning methods for medical images super resolution problems. Irbm 42(2), 120–133 (2021)
Malherbe, T., Hanekom, T., Hanekom, J.: Constructing a three-dimensional electrical model of a living cochlear implant user’s cochlea. Inter. J. Numerical Methods Biomed. Eng. 32(7), e02751 (2016)
NIDCD: Nidcd nidcd fact sheet, hearing and balance: cochlear implants (2019). www.nidcd.nih.gov/health/cochlear-implants, (Accessed 10 Jan 2023)
Noble, J.H., Gifford, R.H., Hedley-Williams, A.J., Dawant, B.M., Labadie, R.F.: Clinical evaluation of an image-guided cochlear implant programming strategy. Audiol. Neurotol. 19(6), 400–411 (2014)
Noble, J.H., et al.: Initial results with image-guided cochlear implant programming in children. Otology Neurotol. Official Publicat. Am. Otolog. Soc. Am. Neurotol. Soc. Euro. Acad. Otology Neurotol. 37(2), e63 (2016)
Noble, J.H., Labadie, R.F., Gifford, R.H., Dawant, B.M.: Image-guidance enables new methods for customizing cochlear implant stimulation strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 21(5), 820–829 (2013)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Park, J., Hwang, D., Kim, K.Y., Kang, S.K., Kim, Y.K., Lee, J.S.: Computed tomography super-resolution using deep convolutional neural network. Phys. Med. Biol. 63(14), 145011 (2018)
Qiu, D., Cheng, Y., Wang, X., Zhang, X.: Multi-window back-projection residual networks for reconstructing covid-19 ct super-resolution images. Comput. Methods Programs Biomed. 200, 105934 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, J., Zhao, Y., Noble, J.H., Dawant, B.M.: Conditional generative adversarial networks for metal artifact reduction in CT images of the ear. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_1
Whiten, D.M.: Electro-anatomical models of the cochlear implant. Ph.D. thesis, Massachusetts Institute of Technology (2007)
You, C., et al.: Ct super-resolution gan constrained by the identical, residual, and cycle learning ensemble (gan-circle). IEEE Trans. Med. Imaging 39(1), 188–203 (2019)
Zhang, D., Banalagay, R., Wang, J., Zhao, Y., Noble, J.H., Dawant, B.M.: Two-level training of a 3d u-net for accurate segmentation of the intra-cochlear anatomy in head cts with limited ground truth training data. In: Medical Imaging 2019: Image Processing, vol. 10949, pp. 45–52. SPIE (2019)
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Liu, Z., Fan, Y., Lou, A., Noble, J.H. (2023). Super-Resolution Segmentation Network for Inner-Ear Tissue Segmentation. In: Wolterink, J.M., Svoboda, D., Zhao, C., Fernandez, V. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2023. Lecture Notes in Computer Science, vol 14288. Springer, Cham. https://doi.org/10.1007/978-3-031-44689-4_2
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