Abstract
Central serous chorioretinopathy (CSCR) is a chorioretinal disorder of the eye characterized by serous detachment of the neurosensory retina at the posterior pole of the eye. CSCR results from the accumulation of subretinal fluid (SRF) due to idiopathic defects at the level of the retinal pigment epithelial (RPE) that allows serous fluid from the choriocapillaris to diffuse into the subretinal space between RPE and neurosensory retinal layers. This condition is presently investigated by clinicians using invasive angiography or non-invasive optical coherence tomography (OCT) imaging. OCT images provide a representation of the fluid underlying the retina, and in the absence of automated segmentation tools, currently only a qualitative assessment of the same is used to follow the progression of the disease. Automated segmentation of the SRF can prove to be extremely useful for the assessment of progression and for the timely management of CSCR. In this paper, we adopt an existing architecture called SegCaps, which is based on the recently introduced Capsule Networks concept, for the segmentation of SRF from CSCR OCT images. Furthermore, we propose an enhancement to SegCaps, which we have termed as DRIP-Caps, that utilizes the concepts of Dilation, Residual Connections, Inception Blocks, and Capsule Pooling to address the defined problem. The proposed model outperforms the benchmark UNet architecture while reducing the number of trainable parameters by 54.21%. Moreover, it reduces the computation complexity of SegCaps by reducing the number of trainable parameters by 37.85%, with competitive performance. The experiments demonstrate the generalizability of the proposed model, as evidenced by its remarkable performance even with a limited number of training samples.
Similar content being viewed by others
References
Bennett G (1955) Central serous retinopathy. Br J Ophthalmol 39(10):605
Wang M, Munch IC, Hasler PW, Prünte C, Larsen M (2008) Central serous chorioretinopathy. Acta ophthalmologica 86(2):126–145
Rao TN, Girish G, Kothari AR, Rajan J (2019) Deep learning based sub-retinal fluid segmentation in central serous chorioretinopathy optical coherence tomography scans. In: 2019 41st Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 978–981
Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA, et al. (1991) Optical coherence tomography. Science 254(5035):1178–1181
Anoop B, Girish G, Sudeep P, Rajan J (2019) Despeckling algorithms for optical coherence tomography images: a review. In: Advanced classification techniques for healthcare analysis, IGI Global, pp 286–310
Menon SN, Reddy VV, Yeshwanth A, Anoop B, Rajan J (2020) A novel deep learning approach for the removal of speckle noise from optical coherence tomography images using gated convolution–deconvolution structure. In: Proceedings of 3rd international conference on computer vision and image processing. Springer, pp 115–126
Hassan SA, Akbar S, Rehman A, Tariq U, Saba T, Abbasi R (2020) Recent developments in detection of central serous retinopathy through imaging and artificial intelligence techniques a review, arXiv:2012.10961
Hassan B, Raja G, Hassan T, Akram MU (2016) Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. JOSA A 33 (4):455–463
Syed AM, Hassan T, Akram MU, Naz S, Khalid S (2016) Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3d retinal surfaces. Comput Methods Programs Biomed 137:1–10
Khalid S, Akram MU, Hassan T, Nasim A, Jameel A (2017) Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images. BioMed research international 2017
Hassan B, Hassan T (2019) Fully automated detection, grading and 3d modeling of maculopathy from oct volumes. In: 2019 2nd International conference on communication, computing and digital systems (C-CODE). IEEE, pp 252–257
Wu M, Fan W, Chen Q, Du Z, Li X, Yuan S, Park H (2017) Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in sd-oct for central serous chorioretinopathy. Biomed Optics Express 8(9):4257– 4274
Novosel J, Wang Z, de Jong H, Van Velthoven M, Vermeer KA, van Vliet LJ (2016) 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). IEEE, pp 702– 705
De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O’Donoghue B, Visentin D, et al. (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine 24(9):1342–1350
Girish G, Anima V, Kothari AR, Sudeep P, Roychowdhury S, Rajan J (2018) A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography b-scans. Comput Methods Programs Biomed 153:105–114
Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge
Gao K, Niu S, Ji Z, Wu M, Chen Q, Xu R, Yuan S, Fan W, Chen Y, Dong J (2019) 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
Teja RV, Manne SR, Goud A, Rasheed MA, Dansingani KK, Chhablani J, Vupparaboina KK, Jana S (2019) Classification and quantification of retinal cysts in oct b-scans: efficacy of machine learning methods. In: 2019 41st Annual International Conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 48–51
Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 23(5):828–841
Moosavi-Dezfooli S-M, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2574–2582
Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427–436
Sabour S, Frosst N, Hinton G (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866
Hinton G, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: International conference on artificial neural networks. Springer, pp 44–51
LeCun Y, Cortes C, Burges CJ (1998) The mnist database, http://yann.lecun.com/exdb/mnist/
http://www.cs.toronto.edu/tijmen/affNIST/. Accessed April 4, 2020. http://www.cs.toronto.edu/tijmen/affNIST/
Krizhevsky A, Hinton G, et al. (2009) Learning multiple layers of features from tiny images
Hinton G, Sabour S, Frosst N (2018) Matrix capsules with em routing
Rajasegaran J, Jayasundara V, Jayasekara S, Jayasekara H, Seneviratne S, Rodrigo R (2019) Deepcaps: Going deeper with capsule networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10725–10733
LaLonde R, Kandel P, Spampinato C, Wallace MB, Bagci U (2020) Diagnosing colorectal polypsin the wild with capsule networks. In: 2020 IEEE 17th International Symposium on BiomedicalImaging (ISBI), IEEE, pp 1086–1090
Xiong Y, Su G, Ye S, Sun Y, Sun Y (2019) Deeper capsule network for complex data. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–8
Shahroudnejad A, Afshar P, Plataniotis KN, Mohammadi A (2018) Improved explainability of capsule networks: Relevance path by agreement. In: 2018 IEEE Global conference on signal and information processing (GlobalSIP). IEEE, pp 549– 553
Phaye SSR, Sikka A, Dhall A, Bathula D (2018) Dense and diverse capsule networks: making the capsules learn better, arXiv:1805.04001
Phaye SSR, Sikka A, Dhall A, Bathula DR (2018) Multi-level dense capsule networks. In: Asian conference on computer vision. Springer, pp 577–592
Jia B, Huang Q (2020) De-capsnet: a diverse enhanced capsule network with disperse dynamic routing. Appl Sci 10(3):884
Edraki M, Rahnavard N, Shah M (2020) Subspace capsule network. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, pp 10745–10753
Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 3129–3133
Adu K, Yu Y, Cai J, Tashi N (2019) Dilated capsule network for brain tumor type classification via mri segmented tumor region. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp 942–947
Wang D, Liu Q. (2018) An optimization view on dynamic routing between capsules
Punjabi A, Schmid J, Katsaggelos AK (2020) Examining the benefits of capsule neural networks, arXiv:2001.10964
Jiménez-Sánchez A, Albarqouni S, Mateus D (2018) Capsule networks against medical imaging data challenges. In: Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis. Springer, pp 150–160
Nair P, Doshi R, Keselj S (2021) Pushing the limits of capsule networks, arXiv preprintarXiv:2103.08074
Patrick MK, Adekoya AF, Mighty AA, Edward B. Y. (2019) Capsule networks–a survey. Journal of King Saud University-computer and information sciences
Dong Z, Lin S (2019) Research on image classification based on capsnet. In: 2019 IEEE 4th Advanced information technology, electronic and automation control conference (IAEAC), vol 1. IEEE, pp 1023–1026
Chauhan A, Babu M, Kandru N, Lokegaonkar S (2018) Empirical study on convergence of capsule networks with various hyperparameters
Paik I, Kwak T, Kim I (2019) Capsule networks need an improved routing algorithm. In: Asian Con-ference on Machine Learning, PMLR, pp 489–502
LaLonde R, Xu Z., Irmakci I., Bagci U (2018) Capsules for biomedical image segmentation. Med Image Anal 68:101889
Bonheur S, Štern D., Payer C, Pienn M, Olschewski H, Urschler M (2019) Matwo-capsnet: a multi-label semantic segmentation capsules network. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 664–672
Kromm C, Rohr K (2019) Inception capsule network for retinal blood vessel segmentation and centerline extraction. bioRxiv, 815555
Zeng T, So HK-H, Lam EY (2020) Redcap: residual encoder-decoder capsule network for holographic image reconstruction. Opt Express 28(4):4876–4887
Duarte K, Rawat Y, Shah M (2019) Capsulevos: semi-supervised video object segmentation using capsule routing. In: Proceedings of the IEEE international conference on computer vision, pp 8480–8489
Duarte K, Rawat Y, Shah M (2018) Videocapsulenet: a simplified network for action detection. In: Advances in neural information processing systems, pp 7610–7619
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122
Chollet F, et al. (2015) keras, github, GitHub repository, https://github.com/fchollet/keras
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pp 265–283
Ozcan A, Bilenca A, Desjardins AE, Bouma BE, Tearney GJ (2007) Speckle reduction in optical coherence tomography images using digital filtering. JOSA A 24(7):1901–1910
Anoop B, Kalmady KS, Udathu A, Siddharth V, Girish G, Kothari AR, Rajan J (2021) A cascaded convolutional neural network architecture for despeckling oct images. Biomedical Signal Processing and Control 66 :102463
Iwai T, Asakura T (1996) Speckle reduction in coherent information processing. Proc IEEE 84(5):765–781
Schmitt JM, Xiang S, Yung KM (1999) Speckle in optical coherence tomography. J Biomed Optics 4(1):95–105
Girish G, Thakur B, Chowdhury SR, Kothari AR, Rajan J (2018) Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model. EEE J Biomed Health Inform 23(1):296–304
Anoop B, Pavan R, Girish G, Kothari AR, Rajan J (2020) Stack generalized deep ensemble learning for retinal layer segmentation in optical coherence tomography images. Biocybern Biomed Eng 40(4):1343–1358
Girish G, Saikumar B, Roychowdhury S, Kothari AR, Rajan J (2019) Depthwise separable convolutional neural network model for intra-retinal cyst segmentation. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2027–2031
Li K, Wu X, Chen DZ, Sonka M (2005) Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell 28(1):119–134
Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208
Garvin MK, Abramoff MD, Wu X, Russell SR, Burns TL, Sonka M (2009) Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans Med Imaging 28(9):1436–1447
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26 (3):297–302
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
Shore J, Johnson R (1980) Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Trans Inf Theory 26(1):26–37
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv:1412.6980
Acknowledgements
This work was funded by the Science and Engineering Research Board (Department of Science and Technology, India) through project funding EMR/2016/002677. The authors are also thankful to Pink City Eye and Retina Center, Jaipur, India for providing the dataset.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pawan, S.J., Sankar, R., Jain, A. et al. Capsule Network–based architectures for the segmentation of sub-retinal serous fluid in optical coherence tomography images of central serous chorioretinopathy. Med Biol Eng Comput 59, 1245–1259 (2021). https://doi.org/10.1007/s11517-021-02364-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-021-02364-4