Abstract
Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.
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
Bogunovic, H., Venhuizen, F., Klimscha, S., Apostolopoulos, S., Bab-Hadiashar, A., et al.: RETOUCH: the retinal OCT fluid detection and segmentation benchmark and challenge. IEEE Trans. Med. Imaging 38(8), 1858–1874 (2019)
Bolte, J.A., et al.: Unsupervised domain adaptation to improve image segmentation quality both in the source and target domain. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1404–1413 (2019)
Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: IEEE International Conference on Computer Vision (ICCV), pp. 9650–9660 (2021)
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 12546–12558 (2020)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML), pp. 1597–1607 (2020)
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 22243–22255 (2020)
Chen, X., He, K.: Exploring simple siamese representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15750–15758 (2021)
Chen, Y., et al.: USCL: pretraining deep ultrasound image diagnosis model through video contrastive representation learning. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 627–637. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_60
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)
Fauw, J.D., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342–1350 (2018)
Fujimoto, J., Swanson, E.: The development, commercialization, and impact of optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 57(9) (2016)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning (ICML), pp. 1180–1189 (2015)
Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 21271–21284 (2020)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9729–9738 (2020)
Khosla, P., et al.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 18661–18673 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
Maunz, A., et al.: Accuracy of a machine-learning algorithm for detecting and classifying choroidal neovascularization on spectral-domain optical coherence tomography. J. Personal. Med. 11(6), 524 (2021)
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Ren, M., Dey, N., Fishbaugh, J., Gerig, G.: Segmentation-renormalized deep feature modulation for unpaired image harmonization. IEEE Trans. Med. Imaging 40(6), 1519–1530 (2021)
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
Sahni, J.N., et al.: A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT. Invest. Ophthalmol. Vis. Sci. 60(11), PB094–PB094 (2019)
Schlegl, T., et al.: Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125(4), 549–558 (2018)
Schmidt-Erfurth, U., Waldstein, S.M.: A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog. Retin. Eye Res. 50, 1–24 (2016)
Seeböck, P., et al.: Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 605–609 (2019)
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)
Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gomariz, A. et al. (2022). Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-16452-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16451-4
Online ISBN: 978-3-031-16452-1
eBook Packages: Computer ScienceComputer Science (R0)
