Segmentation of OCT and OCT-A Images using Convolutional Neural Networks

  • Fernanda Cisneros-Guzmán Universidad Autónoma de Querétaro
  • Manuel Toledano-Ayala Universidad Autónoma de Querétaro
  • Saúl Tovar-Arriaga Universidad Autónoma de Querétaro
  • Edgar A. Rivas-Araiza Universidad Autótoma de Querétaro
Keywords: OCT-A segmentation, ResU-Net, FCN segmentation, Convolutional Neural Network

Abstract

Segmentation is vital in Optical Coherence Tomography Angiography (OCT-A) images. The separation and distinction of the different parts that build the macula simplify the subsequent detection of observable patterns/illnesses in the retina. In this work, we carried out multi-class image segmentation where the best characteristics are highlighted in the appropriate plexuses by comparing different neural network architectures, including U-Net, ResU-Net, and FCN. We focus on two critical zones: retinal vasculature (RV) and foveal avascular zone (FAZ). The precision obtained from the RV and FAZ segmentation over 316 OCT-A images from the OCT-A 500 database at 93.21% and 92.59%, where the FAZ was segmented with an accuracy of 99.83% for binary classification.

Downloads

Download data is not yet available.

References

Wons J, Pfau M, Wirth MA, Freiberg FJ, et al. Optical coherence tomography angiography of the foveal avascular zone in retinal vein occlusion. Ophthalmologica [Internet]. 2016; 235:195-202. Available from: https://doi.org/10.1159/000445482

Guo M, Zhao M, Cheong AMY, Dai H, et al. Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning. Vis Comput Ind Biomed Art [Internet]. 2019;2:21. Available from: https://doi.org/10.1186/s42492-019-0031-8

Leopold HA, Orchard J, Zelek JS, Lakshminarayanan V. PixelBNN: Augmenting the Pixelcnn with Batch Normalization and the Presentation of a Fast Architecture for Retinal Vessel Segmentation. J Imaging [Internet]. 2019;5(2):26. Available from: https://doi.org/10.3390/jimaging5020026

Zhang Y, Chung ACS. Deep Supervision with Additional Labels for Retinal Vessel Segmentation Task. In: Frangi A, Schnabel J, Davatzikos C, Alberola-López C, et al. (eds). Medical Image Computing and Computer Assisted Intervention- MICCAI 2018 [Internet]. Granada, Spain: Springer; 2018:83-91. Available from: https://doi.org/10.1007/978-3-030-00934-2_10

Xiao X, Lian S, Luo Z, Li S. Weighted Res-UNet for High-Quality Retina Vessel Segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME) [Internet]. Hangzhou, China: IEEE; 2018: 327-331. Available from: https://doi.org/10.1109/ITME.2018.00080

Son J, Park SJ, Jung KH. Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks. J Digit Imaging [Internet]. 2019;32(3):499–512. Available from: https://doi.org/10.1007/s10278-018-0126-3

Jayabalan GS, Bille JF. The Development of Adaptive Optics and Its Application in Ophthalmology. In: Bille J. (eds). High Resolution Imaging in Microscopy and Ophthalmology [Internet]. Cham, Switzerland: Springer; 2019: 339–358p.

Taher F, Kandil H, Mahmoud H, Mahmoud A, et al. A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography. Appl Sci [Internet]. 2021;11(9):4158. Available from: https://doi.org/10.3390/app11094158

Spaide RF, Fujimoto JG, Waheed NK, Sadda SR, et al. Optical coherence tomography angiography. Vol. 64, Prog Retin Eye Res [Internet]. 2018;64:1–55. Available from: https://doi.org/10.1016/j.preteyeres.2017.11.003

de Carlo TE, Romano A, Waheed NK, Duker JS. A review of optical coherence tomography angiography (OCTA). Int J Retin Vitr [Internet]. 2015;1:5. Available from: https://doi.org/10.1186/s40942-015-0005-8

Wylęgała A, Teper S, Dobrowolski D, Wylęgała E. Optical coherence angiography: A review. Medicine [Internet]. 2016;95(41):e4907. Available from: https://doi.org/10.1097/MD.0000000000004907

Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal [Internet]. 2015;19(1):46–57. Available from: https://doi.org/10.1016/j.media.2014.08.002

Lau QP, Lee L, Hsu W, Wong TY. Simultaneously Identifying All True Vessels from Segmented Retinal Images. IEEE Trans Biomed Eng [Internet]. 2013;60(7):1851-1858. Available from: https://doi.org/10.1109/tbme.2013.2243447

Ghazal M, Al Khalil Y, Alhalabi M, Fraiwan L, et al. 9-Early detection of diabetics using retinal OCT images. In: El-Baz AS, Suri JS (eds). Diabetes and Retinopathy [Internet]. United States: Elsevier; 2020. 173–204p. Available from: https://doi.org/10.1016/B978-0-12-817438-8.00009-2

Li W, Zhang Y, Ji Z, Xie K, et al. IPN-V2 and OCTA-500: Methodology and Dataset for Retinal Image Segmentation, distributed by Cornell University [Internet]. 2020. Available from:

https://doi.org/10.48550/arXiv.2012.07261

Bates NM, Tian J, Smiddy WE, Lee W-H, et al. Relationship between the morphology of the foveal avascular zone, retinal structure, and macular circulation in patients with diabetes mellitus. Sci Rep [Internet]. 2018;8:5355. Available from: https://doi.org/10.1038/s41598-018-23604-y

Ronneberger O, Fischer P, Brox T. U-net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds). Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 [Internet]. Munich, Germany: Springer; 2015: 234-241. Available from: https://doi.org/10.1007/978-3-319-24574-4_28

Sappa LB, Okuwobi IP, Li M, Zhang Y, et al. RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network. J Digit Imaging [Internet]. 2021;34(3):691–704. Available from: https://doi.org/10.1007/s10278-021-00459-w

Rasamoelina AD, Adjailia F, Sinčák P. A Review of Activation Function for Artificial Neural Network. In: 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI) [Internet]. Herlany, Slovakia: IEEE; 2020: 281-286. Available from: https://doi.org/10.1109/SAMI48414.2020.9108717

Qi W, Wei M, Yang W, Xu C, et al. Automatic Mapping of Landslides by the ResU-Net. Remote Sens [Internet]. 2020;12(15):2487. Available from: https://doi.org/10.3390/rs12152487

Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 [Internet]. Lille, France: JMLR; 2015: 448–456. Available from: https://dl.acm.org/doi/10.5555/3045118.3045167

He Y, Carass A, Liu Y, Jedynak BM, et al. Fully Convolutional Boundary Regression for Retina OCT Segmentation. In: Shen D, Liu T, Peters TM, Staib LH, et al. (eds). Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019 [Internet]. Shenzhen, China: Springer; 2019: 120–128. Available from: https://doi.org/10.1007/978-3-030-32239-7_14

Liu X, Song L, Liu S, Zhang Y. A review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability [Internet]. 2021;13(3):1224. Available from: https://doi.org/10.3390/su13031224

Published
2022-11-11
How to Cite
Cisneros-Guzmán, F., Toledano-Ayala, M., Tovar-Arriaga, S., & Rivas-Araiza, E. A. (2022). Segmentation of OCT and OCT-A Images using Convolutional Neural Networks. Mexican Journal of Biomedical Engineering, 43(3), 15-24. Retrieved from https://rmib.mx/index.php/rmib/article/view/1280
Section
Research Articles

Most read articles by the same author(s)