Abstract
Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology. However, OCT images are strongly corrupted by noise, limiting their interpretation. Current OCT denoisers leverage assumptions on noise distributions or generate targets for training deep supervised denoisers via averaging of repeat acquisitions. However, recent self-supervised advances allow the training of deep denoising networks using only repeat acquisitions without clean targets as ground truth, reducing the burden of supervised learning. Despite the clear advantages of self-supervised methods, their use is precluded as OCT shows strong structural deformations even between sequential scans of the same subject due to involuntary eye motion. Further, direct nonlinear alignment of repeats induces correlation of the noise between images. In this paper, we propose a joint diffeomorphic template estimation and denoising framework which enables the use of self-supervised denoising for motion deformed repeat acquisitions, without empirically registering their noise realizations. Strong qualitative and quantitative improvements are achieved in denoising OCT images, with generic utility in any imaging modality amenable to multiple exposures.
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 subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
For [18], Algorithm 1 in the paper suggests that lower is better. However, their code negates the final correlation value, thus making higher better. We do the same to maintain consistency with their convention.
References
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)
Avants, B.B., et al.: The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49(3), 2457–2466 (2010)
Bashkirova, D., Usman, B., Saenko, K.: Adversarial self-defense for cycle-consistent GANs. In: Advances in Neural Information Processing Systems, pp. 637–647 (2019)
Batson, J., Royer, L.: Noise2self: blind denoising by self-supervision. In: International Conference on Machine Learning, pp. 524–533 (2019)
Broaddus, C., Krull, A., Weigert, M., Schmidt, U., Myers, G.: Removing structured noise with self-supervised blind-spot networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 159–163. IEEE (2020)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 60–65. IEEE (2005)
Buchholz, T.O., Jordan, M., Pigino, G., Jug, F.: Cryo-CARE: content-aware image restoration for cryo-transmission electron microscopy data. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 502–506. IEEE (2019)
Chu, C., Zhmoginov, A., Sandler, M.: Cyclegan, a master of steganography. arXiv preprint arXiv:1712.02950 (2017)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Devalla, S.K., et al.: A deep learning approach to denoise optical coherence tomography images of the optic nerve head. Sci. Rep. 9(1), 1–13 (2019)
Dey, N., Messinger, J., Smith, R.T., Curcio, C.A., Gerig, G.: Robust non-negative tensor factorization, diffeomorphic motion correction, and functional statistics to understand fixation in fluorescence microscopy. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science, vol. 11764, pp. 658–666. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_73
Gisbert, G., Dey, N., Ishikawa, H., Schuman, J., Fishbaugh, J., Gerig, G.: Improved denoising of optical coherence tomography via repeated acquisitions and unsupervised deep learning. Invest. Ophthalmol. Vis. Sci. 61(9), PB0035 (2020)
Halupka, K.J., et al.: Retinal optical coherence tomography image enhancement via deep learning. Biomed. Opt. Exp. 9(12), 6205–6221 (2018)
Hendriksen, A.A., Pelt, D.M., Batenburg, K.J.: Noise2inverse: self-supervised deep convolutional denoising for linear inverse problems in imaging. arXiv preprint arXiv:2001.11801 (2020)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, S151–S160 (2004)
Kafieh, R., Rabbani, H., Selesnick, I.: Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imaging 34(5), 1042–1062 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kong, X., Li, K., Yang, Q., Wenyin, L., Yang, M.H.: A new image quality metric for image auto-denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2888–2895 (2013)
Krull, A., Buchholz, T.O., Jug, F.: Noise2void-learning denoising from single noisy images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2129–2137 (2019)
Laine, S., Karras, T., Lehtinen, J., Aila, T.: High-quality self-supervised deep image denoising. In: Advances in Neural Information Processing Systems, pp. 6970–6980 (2019)
Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: ICML, pp. 2971–2980 (2018)
Maggioni, M., Katkovnik, V., Egiazarian, K., Foi, A.: Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22(1), 119–133 (2012)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Qiu, B., et al.: Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function. Biomed. Opt. Exp. 11(2), 817–830 (2020)
Ravier, M., et al.: Analysis of morphological changes of lamina cribrosa under acute intraocular pressure change. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) Medical Image Computing and Computer Assisted Intervention – ICCAI 2018. Lecture Notes in Computer Science, vol. 11071, pp. 364–371. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_41
Romo-Bucheli, D., et al.: Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomed. Opt. Exp. 11(1), 346–363 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Zhang, Y., et al.: A poisson-gaussian denoising dataset with real fluorescence microscopy images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11710–11718 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)
Acknowledgments
This work was supported by NIH grants 1R01EY027948-01 and 2R01EY013178-15. HPC resources used for this research provided by grant NSF MRI-1229185.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gisbert, G., Dey, N., Ishikawa, H., Schuman, J., Fishbaugh, J., Gerig, G. (2020). Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-63419-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63418-6
Online ISBN: 978-3-030-63419-3
eBook Packages: Computer ScienceComputer Science (R0)