nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning

verfasst von : Suman Sedai, Bhavna Antony, Dwarikanath Mahapatra, Rahil Garnavi

Erschienen in: Computational Pathology and Ophthalmic Medical Image Analysis

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming

Nächstes Kapitel cGAN-Based Lacquer Cracks Segmentation in ICGA Image

Acton, J.H., Smith, R.T., Hood, D.C., Greenstein, V.C.: Relationship between retinal layer thickness and the visual field in early age-related macular degeneration. Investig. Ophthalmol. Vis. Sci. 53(12), 7618–7624 (2012)CrossRef

Apostolopoulos, S., Zanet, S.D., Ciller, C., Wolf, S., Sznitman, R.: Pathological OCT retinal layer segmentation using branch residual u-shape networks. CoRR abs/1707.04931 (2017)

Carass, A., Lang, A., Hauser, M., Calabresi, P.A., Ying, H.S., Prince, J.L.: Multiple-object geometric deformable model for segmentation of macular OCT. Biomed. Opt. Express 5(4), 1062 (2014)CrossRef

Chiu, S.J., Li, X.T., Nicholas, P., Toth, C.A., Izatt, J.A., Farsiu, S.: Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Optics Express 18(18), 19413–19428 (2010)CrossRef

Fang, L., Cunefare, D., Wang, C., Guymer, R.H., Li, S., Farsiu, S.: Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed. Opt. Express 8(5), 2732 (2017)CrossRef

Farsiu, S., et al.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Opthamalogy 121(1), 162–172 (2014)CrossRef

Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on International Conference on Machine Learning, pp. 1050–1059 (2016)

Garvin, M.K., Abràmoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009)CrossRef

Iglesias, J.E., Sabuncu, M.R., Leemput, K.V.: Improved inference in bayesian segmentation using monte carlo sampling: application to hippocampal subfield volumetry. Med. Image Anal. 17(7), 766–778 (2013)CrossRef

10.

Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, Honolulu, HI, USA, 21–26 July 2017, pp. 1175–1183 (2017)

11.

Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Neural Information Processing Systems (NIPS) (2017)

12.

Lang, A., et al.: Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express 4(7), 1133–1152 (2013)CrossRef

13.

Mishra, A., Wong, A., Bizheva, K., Clausi, D.A.: Intra-retinal layer segmentation in optical coherence tomography images. Opt. Express 17(26), 23719–28 (2009)CrossRef

14.

Novosel, J., Thepass, G., Lemij, H.G., de Boer, J.F., Vermeer, K.A., van Vliet, L.J.: Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography. Med. Image Anal. 26(1), 146–158 (2015)CrossRef

15.

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_28CrossRef

16.

Roy, A.G., et al.: Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network. CoRR abs/1704.02161 (2017)

17.

Sedai, S., Tennakoon, R., Roy, P., Cao, K., Garnavi, R.: Multi-stage segmentation of the fovea in retinal fundus images using fully convolutional neural networks. In: ISBI, pp. 1083–1086, April 2017

18.

Springenberg, J.T., Klein, A., Falkner, S., Hutter, F.: Bayesian optimization with robust Bayesian neural networks. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 4134–4142. Curran Associates, Inc. (2016)

19.

Srinivasan, P.P., et al.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568–3577 (2014)CrossRef

20.

Vermeer, K.A., van der Schoot, J., Lemij, H.G., de Boer, J.F.: Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images. Biomed. Opt. Express 2(6), 1743–1756 (2011)CrossRef

Titel: Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning
verfasst von: Suman Sedai
Bhavna Antony
Dwarikanath Mahapatra
Rahil Garnavi
Verlag: Springer International Publishing
Buch: Computational Pathology and Ophthalmic Medical Image Analysis
Print ISBN: 978-3-030-00948-9

Electronic ISBN: 978-3-030-00949-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00949-6_26

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner