Abstract
Central serous retinopathy is a serious retinal disease. Retinal layer segmentation for this disease can help ophthalmologists to provide accurate diagnosis and proper treatment for patients. In order to detect surfaces in optical coherence tomography images with pathological changes, an automatic method is reported by combining random forests and a live wire algorithm. First, twenty four features are designed for the random forest classifiers to find initial surfaces. Then, a live wire algorithm is proposed to accurately detect surfaces between retinal layers even though OCT images with fluids are of low contrast and layer boundaries are blurred. The proposed method was evaluated on 24 spectral domain OCT images with central serous retinopathy. The experimental results showed that the proposed method outperformed the state-of-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Staurenghi, G., Sadda, S., Chakravarthy, U., Spaide, R.F., et al.: Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN OCT consensus. Ophthalmology 121(8), 1572–1578 (2014)
Field, M.G., Elner, V.M., Park, S., Hackel, R., Heckenlively, J.R., Elner, S.G., Petty, H.R.: Detection of retinal metabolic stress due to central serous retinopathy. Retina 29(8), 1162 (2009). (Philadelphia, Pa.)
Hassan, B., Raja, G., Hassan, T., Akram, M.U.: Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. JOSA A 33(4), 455–463 (2016)
Matsumoto, H., Sato, T., Kishi, S.: Outer nuclear layer thickness at the fovea determines visual outcomes in resolved central serous chorioretinopathy. Am. J. Ophthalmol. 148(1), 105–110 (2009)
Ahlers, C., Geitzenauer, W., Stock, G., Golbaz, I., Schmidt-Erfurth, U., Prünte, C.: Alterations of intraretinal layers in acute central serous chorioretinopathy. Acta Ophthalmol. 87(5), 511–516 (2009)
Novosel, J., Wang, Z., de Jong, H., van Velthoven, M., Vermeer, K.A., van Vliet, L.J.: Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 702–705. IEEE (2016)
Iowa reference algorithms: Human and murine 3d OCT retinal layer analysis and display (Iowa institute for biomedical imaging, Iowa city, IA)
Garvin, M.K., Abramoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009)
Song, Q., Bai, J., Garvin, M.K., Sonka, M., Buatti, J.M., Wu, X.: Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imaging 32(2), 376–386 (2013)
Shi, F., Chen, X., Zhao, H., Zhu, W., Xiang, D., Gao, E., Sonka, M., Chen, H.: Automated 3-d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments. IEEE Trans. Med. Imaging 34(2), 441–452 (2015)
Lang, A., Carass, A., Hauser, M., Sotirchos, E.S., Calabresi, P.A., Ying, H.S., Prince, J.L.: Retinal layer segmentation of macular oct images using boundary classification. Biomed. Opt. Express 4(7), 1133–1152 (2013)
Acknowledgment
This work has been supported in part by the National Basic Research Program of China (973 Program) under Grant 2014CB748600, and in part by the National Natural Science Foundation of China (NSFC) under Grant 81371629, 61401293, 61401294, 81401451, 81401472.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xiang, D., Chen, G., Shi, F., Zhu, W., Chen, X. (2017). Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-67561-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67560-2
Online ISBN: 978-3-319-67561-9
eBook Packages: Computer ScienceComputer Science (R0)