Abstract
In medical imaging, high-resolution can be crucial for identifying pathologies and subtle changes in tissue structure. However, in many scenarios, achieving high image resolution can be limited by physics or available technology. In this paper, we aim to develop an automatic and fast approach to increasing the resolution of Optical Coherence Tomography (OCT) images using the data available, without any additional information or repeated scans. We adapt a fully connected deep learning network for the super-resolution task, allowing multi-scale similarity to be considered, and create a training and testing set of more than 40,000 sample patches from retinal OCT data. Testing our model, we achieve an impressive root mean squared error of 5.847 and peak signal-to-noise ratio (PSNR) of 33.28 dB averaged over 8282 samples. This represents a mean improvement in PSNR of 3.2 dB over nearest neighbour and 1.4 dB over bilinear interpolation. The results achieved so far improve over commonly used fast techniques for increasing resolution and are very encouraging for further development towards fast OCT super-resolution. The ability to increase quickly the resolution of OCT as well as other medical images has the potential to impact significantly on medical imaging at point of care, allowing significant small details to be revealed efficiently and accurately for inspection by clinicians and graders and facilitating earlier and more accurate diagnosis of disease.
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References
Boonarpha, N.: Choroidal structure and function in chronic retinal diseases. Doctoral dissertation, University of Liverpool (2016)
Boppart, S.A., Herrmann, J., Pitris, C., Stamper, D.L., Brezinski, M.E., Fujimoto, J.G.: High-resolution optical coherence tomography-guided laser ablation of surgical tissue. J. Surg. Res. 82(2), 275–284 (1999)
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE T. Pattern Anal. 38(2), 295–307 (2016)
Fujimoto, J., Boppart, S.A., Tearney, G., Bouma, B., Pitris, C., Brezinski, M.: High resolution in vivo intra-arterial imaging with optical coherence tomography. Heart 82(2), 128–133 (1999)
Gargesha, M., Jenkins, M.W., Wilson, D.L., Rollins, A.M.: High temporal resolution oct using image-based retrospective gating. Opt. Exp. 17(13), 10786–10799 (2009)
Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE T. Pattern Anal. 32(6), 1127–1133 (2010)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint (2016)
Miller, D., Kocaoglu, O., Wang, Q., Lee, S.: Adaptive optics and the eye (super resolution OCT). Eye 25(3), 321 (2011)
Nassif, N., et al.: In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve. Opt. Exp. 12(3), 367–376 (2004)
Pickup, L.C., Roberts, S.J., Zisserman, A.: A sampled texture prior for image super-resolution. In: Advances in Neural Information Processing Systems, pp. 1587–1594 (2004)
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
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
Timofte, R., De, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1920–1927. IEEE (2013)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
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Xu, Y., Williams, B.M., Al-Bander, B., Yan, Z., Shen, Yc., Zheng, Y. (2018). Improving the Resolution of Retinal OCT with Deep Learning. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_30
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DOI: https://doi.org/10.1007/978-3-319-95921-4_30
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