Abstract
In cases of optic disc swelling, volumetric measurements and shape features are promising to evaluate the severity of the swelling and to differentiate the cause. However, previous studies have mostly focused on the use of volumetric spectral-domain optical coherence tomography (OCT), which is not always available in non-ophthalmic clinics and telemedical settings. In this work, we propose the use of a deep-learning-based approach (more specifically, an adaptation of a feature pyramid network, FPN) to obtain total-retinal-thickness (TRT) maps (as would normally be obtained from OCT) from more readily available 2D color fundus photographs. From only these thickness maps, we are able to compute both volumetric measures of swelling for quantification of the location/degree of swelling and 3D statistical shape measures for quantification of optic-nerve-head morphology. Evaluating our proposed approach (using nine-fold cross validation) on 102 paired color fundus photographs and OCT images (with the OCT acting as the ground truth) from subjects with various levels of optic disc swelling, we achieved significantly smaller errors and significantly larger linear correlations of both the volumetric measures and shape measures than that which would be obtained using a U-Net approach. The proposed method has great potential to make 3D ONH shape analysis possible even in situations where only color fundus photographs are available; these 3D shape measures can also be beneficial to help differentiate causes of optic disc swelling.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, J.K., Kardon, R.H., Kupersmith, M.J., Garvin, M.K.: Automated quantification of volumetric optic disc swelling in papilledema using spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53(7), 4069–4075 (2012)
Sibony, P.A., Kupersmith, M.J., James Rohlf, F.: Shape analysis of the peripapillary RPE layer in papilledema and ischemic optic neuropathy. Invest. Ophthalmol. Vis. Sci. 52(11), 7987–7995 (2011)
Wang, J.K., Sibony, P.A., Kardon, R.H., Kupersmith, M.J., Garvin, M.K.: Semi-automated 2D Bruch’s membrane shape analysis in papilledema using spectral-domain optical coherence tomography. In: Proceedings of the SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 9417, p. 941721 (2015)
Vuong, L.N., Hedges, T.R.: Optical coherence tomography and optic nerve edema. In: Grzybowski, A., Barboni, P. (eds.) OCT and Imaging in Central Nervous System Diseases, pp. 147–167. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26269-3_9
Malhotra, K., Patel, M.D., Shirazi, Z., Moss, H.E., Moss, H.E.: Association between peripapillary Bruch’s membrane shape and intracranial pressure: Effect of image acquisition pattern and image analysis method, a preliminary study. Front. Neurol. 9(December), 1137 (2018)
Wang, J.K., Thurtell, M.J., Kardon, R.H., Garvin, M.K.: Differentiation of papilledema from non-arteritic anterior ischemic optic neuropathy (NAION) using 3D retinal morphological features of optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 61(7), 3950 (2020). E-Abstract
Tang, L., Kardon, R.H., Wang, J.K., Garvin, M.K., Lee, K., Abrà moff, M.D.: Quantitative evaluation of papilledema from stereoscopic color fundus photographs. Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012)
Agne, J., Wang, J.K., Kardon, R.H., Garvin, M.K.: Determining degree of optic nerve edema from color fundus photography. In: Proceedings of the SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, p. 94140F (2015)
Johnson, S.S., Wang, J.-K., Islam, M.S., Thurtell, M.J., Kardon, R.H., Garvin, M.K.: Local estimation of the degree of optic disc swelling from color fundus photography. In: Stoyanov, D., et al. (eds.) OMIA/COMPAY -2018. LNCS, vol. 11039, pp. 277–284. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00949-6_33
Johnson, S.J., Islam, M.S., Wang, J.K., Matthew, T.J., Kardon, R.H., Garvin, M.K.: Deep-learning-based estimation of regional volumetric information from 2D fundus photography in cases of optic disc swelling. Invest. Ophthalmol. Vis. Sci. 60(9), 3597 (2019). E-Abstract
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
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2117–2125 (2017)
Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6399–6408 (2019)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Acknowledgments
This study was supported, in part, by the Department of Veterans Affairs Merit Award I01 RX001786 and the National Institutes of Health R01 EY023279.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Islam, M.S., Wang, JK., Deng, W., Thurtell, M.J., Kardon, R.H., Garvin, M.K. (2020). Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling. 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_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-63419-3_14
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)