Abstract
Assessment of serous retinal detachment (SRD) plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an unsupervised method, called Gaussian distribution prior based Multi-view Self-supervised Learning (G-MSL), for the segmentation of SRD, in spectral domain optical coherence tomography (SD-OCT) images. We firstly count the Gaussian distribution prior for each targeted retinal layer from normal SD-OCT images. Then the Gaussian distribution prior-based fitting detects the abnormal pixels belonging to SRD in each targeted retinal layer. The generated coarse SRD region masks are used for self-supervised learning to optimize the SRD regions. The fully connected conditional random field is applied to obtain the SRD segmentation results. To improve the robustness of the proposed method for 3D SD-OCT volumes, we repeatedly carry out the above-mentioned operations from another view. The final segmentation results are obtained by getting the union of the results of multiple views. Experimental results on 20 subjects with CSC demonstrate that the proposed method can achieve the average dice similarity coefficient of 91.69%. G-MSL shows enough potential for the improvements of the clinical CSC evaluation and achieves higher segmentation accuracy than the existing supervised deep learning methods when the training set is not very large.
This study was supported in part by National Natural Science Foundation of China (62172223, 61671242), in part by Key R&D Program of Jiangsu Science and Technology Department (BE2018131) and the Fundamental Research Funds for the Central Universities (30921013105).
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
Similar content being viewed by others
References
Semeraro, F., Morescalchi, F., Russo, A., et al.: Central serous chorioretinopathy: pathogenesis and management. Clin. Ophthalmol. 13, 2341–2352 (2019)
Wu, M., Fan, W., Chen, Q., et al.: Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy. Biomed. Opt. Exp. 8(9), 4257 (2017)
Ji, Z., et al.: Beyond retinal layers: a large blob detection for subretinal fluid segmentation in SD-OCT images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 372–380. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_42
Sun, Y., Niu, S., Dong, J., Chen, Y.: 3D level set method via local structure similarity factor for automatic neurosensory retinal detachment segmentation in retinal SD-OCT images. In: Knight, K., Zhang, C., Holmes, G., Zhang, M.-L. (eds.) ICAI 2019. CCIS, vol. 1001, pp. 83–92. Springer, Singapore (2019). https://doi.org/10.1007/978-981-32-9298-7_7
Sun, Z., Chen, H., Shi, F., et al.: An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images. Sci. Rep. 6(1), 21739 (2016)
Roy, A.G., Conjeti, S., Karri, S.P.K., et al.: ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627–3642 (2017)
Gao, K., Niu, S., Ji, Z., et al.: Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images. Comput. Methods Programs Biomed. 176, 69–80 (2019)
Bekalo, L., Niu, S., He, X.J., et al.: Automated 3-D retinal layer segmentation from SD-OCT images with neurosensory retinal detachment. IEEE Access 7, 14894–14907 (2019)
Yang, J., Ji, Z., Niu, S., et al.: RMPPNet: residual multiple pyramid pooling network for subretinal fluid segmentation in SD-OCT images. OSA Continuum 3(7), 1751 (2020)
Gao, Z., Wang, X., Li, Y.: Automatic segmentation of macular edema in retinal OCT images using improved U-Net++. Appl. Sci. 10(16), 5701 (2020)
Jin, Q., Meng, Z., Pham, T.D., et al.: DUNet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149–162 (2019)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. Adv. Neural. Inf. Process. Syst. 24, 109–117 (2011)
Zhang, Y., et al.: Robust layer segmentation against complex retinal abnormalities for en face OCTA generation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 647–655. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_62
Apostolopoulos, S., De Zanet, S., Ciller, C., Wolf, S., Sznitman, R.: Pathological OCT retinal layer segmentation using branch residual U-shape networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 294–301. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_34
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
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
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Huang, H., Lin, L., Tong, R., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, S., Zhang, Y., Li, M., Ji, Z., Yuan, S., Chen, Q. (2022). Gaussian Distribution Prior Based Multi-view Self-supervised Learning for Serous Retinal Detachment Segmentation. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-02444-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02443-6
Online ISBN: 978-3-031-02444-3
eBook Packages: Computer ScienceComputer Science (R0)