Abstract
The choroid layer, situated between the retina and sclera, is a tissue layer that contains blood vessels. Optical coherence tomography (OCT) is a method that utilizes light for imaging purposes to capture detailed images of this specific part of the retina. Although there have been notable advancements, the automated choroid segmentation persists difficult due to the inherently low contrast of OCT images. Handcrafted features, which provide domain-specific knowledge, and convolutional neural network (CNN) methods, which handle large sets of general features, are both employed in addressing this challenge. There is a plea to merge these two different classes of feature-generation methods. The challenge is to form a combined set of features that can outperform either feature extraction method. We proposed a cascaded method for choroid layer segmentation that logically combines a CNN feature set with handcrafted features. Our method used handcrafted features, Gabor features, Haar features, and gray-level co-occurrence features due to the robustness to segment low-contrast images. A support vector machine was independently trained using the CNN feature set and handcrafted feature set, which were then linearly combined for the final choroid segmentation. The method under consideration was assessed using a dataset comprising 525 images. Furthermore, we introduced two metrics to quantitatively evaluate the thickness of the layer: (i) the pixel-wise error in the segmentation and (ii) the average error in the generated thickness map. Through experimentation, the results demonstrated that our proposed method successfully accomplished the intended objective, a remarkable accuracy of 97 percent, with a mean error rate of 2.84. Moreover, it outperformed existing state-of-the-art segmentation methods.
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Ali, S.G., Ali, R., Sheng, B., Chen, Y., Li, H., Yang, P., Li, P., Jung, Y., Zhu, F., Lu, P., et al.: Experimental protocol designed to employ nd: Yag laser surgery for anterior chamber glaucoma detection via ubm. IET Image Process. 5, 58 (2022)
Ali, S.G., Chen, Y., Sheng, B., Li, H., Wu, Q., Yang, P., Muhammad, K., Yang, G.: Cost-effective broad learning-based ultrasound biomicroscopy with 3D reconstruction for ocular anterior segmentation. Multimedia Tools Appl. 80(28), 35105–35122 (2021)
Alonso-Caneiro, D., Kugelman, J., Hamwood, J., Read, S.A., Vincent, S.J., Chen, F.K., Collins, M.J.: Automatic retinal and choroidal boundary segmentation in oct images using patch-based supervised machine learning methods. In: Asian Conference on Computer Vision, pp. 215–228. Springer (2018)
Alonso-Caneiro, D., Read, S.A., Collins, M.J.: Automatic segmentation of choroidal thickness in optical coherence tomography. Biomed. Opt. Express 4(12), 2795–2812 (2013)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Ben-Cohen, A., Mark, D., Kovler, I., Zur, D., Barak, A., Iglicki, M., Soferman, R.: Retinal layers segmentation using fully convolutional network in OCT images. In: RSIP Vision, pp. 1–8 (2017)
Burlina, P., Pacheco, K.D., Joshi, N., Freund, D.E., Bressler, N.M.: Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput. Biol. Med. 82, 80–86 (2017)
Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170–1176 (2017)
Chen, M., Wang, J., Oguz, I., VanderBeek, B.L., Gee, J.C.: Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks. In: Fetal, Infant and Ophthalmic Medical Image Analysis, pp. 177–184. Springer (2017)
Chen, Q., Fan, W., Niu, S., Shi, J., Shen, H., Yuan, S.: Automated choroid segmentation based on gradual intensity distance in hd-oct images. Opt. Express 23(7), 8974–8994 (2015)
Danesh, H., Kafieh, R., Rabbani, H., Hajizadeh, F.: Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts. In: Computational and Mathematical Methods in Medicine (2014)
Djunaidi, K., Agtriadi, H.B., Kuswardani, D., Purwanto, Y.S.: Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery. Indones. J. Electr. Eng. Comput. Sci. 22(2), 187–192 (2020)
Fang, L., Cunefare, D., Wang, C., Guymer, R.H., Li, S., Farsiu, S.: Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed. Opt. Express 8(5), 2732–2744 (2017)
Gopinath, K., Rangrej, S.B., Sivaswamy, J.: A deep learning framework for segmentation of retinal layers from OCT images. In: IAPR Asian Conference on Pattern Recognition, pp. 888–893 (2017)
He, F., Chun, R.K.M., Qiu, Z., Yu, S., Shi, Y., To, C.H., Chen, X.: Choroid segmentation of retinal oct images based on cnn classifier and l2-lq fitter. Comput. Math. Methods Med. 2, 56 (2021)
He, Y., Carass, A., Yun, Y., Zhao, C., Jedynak, B.M., Solomon, S.D., Saidha, S., Calabresi, P.A., Prince, J.L.: Towards topological correct segmentation of macular OCT from cascaded FCNs. In: Fetal, Infant and Ophthalmic Medical Image Analysis, pp. 202–209 (2017)
Hsia, W.P., Tse, S.L., Chang, C.J., Huang, Y.L.: Automatic segmentation of choroid layer using deep learning on spectral domain optical coherence tomography. Appl. Sci. 11(12), 5488 (2021)
Hu, Z., Wu, X., Ouyang, Y., Ouyang, Y., Sadda, S.R.: Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans. Invest. Ophthalmol. Vis. Sci. 54(3), 1722–1729 (2013)
Ishikawa, H., Stein, D.M., Wollstein, G., Beaton, S., Fujimoto, J.G., Schuman, J.S.: Macular segmentation with optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005)
Kajić, V., Esmaeelpour, M., Považay, B., Marshall, D., Rosin, P.L., Drexler, W.: Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model. Biomed. Opt. Express 3(1), 86–103 (2012)
Keller, B., Cunefare, D., Grewal, D.S., Mahmoud, T.H., Izatt, J.A., Farsiu, S.: Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images. J. Biomed. Opt. 21(7), 076015 (2016)
Khaing, T.T., Okamoto, T., Ye, C., Mannan, M.A., Yokouchi, H., Nakano, K., Aimmanee, P., Makhanov, S.S., Haneishi, H.: Choroidnet: a dense dilated u-net model for choroid layer and vessel segmentation in optical coherence tomography images. IEEE Access 9, 150,951–150,965 (2021)
Kugelman, J., Alonso-Caneiro, D., Read, S.A., Hamwood, J., Vincent, S.J., Chen, F.K., Collins, M.J.: Automatic choroidal segmentation in oct images using supervised deep learning methods. Sci. Rep. 9(1), 1–13 (2019)
Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol. Retina 1(4), 322–327 (2017)
Li, Q., Li, S., He, Z., Guan, H., Chen, R., Xu, Y., Wang, T., Qi, S., Mei, J., Wang, W.: Deepretina: layer segmentation of retina in oct images using deep learning. Transl. Vis. Sci. Technol. 9(2), 61–61 (2020)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP, vol. 1, pp. 900–903 (2002)
Liu, X., Bi, L., Xu, Y., Feng, D., Kim, J., Xu, X.: Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images. Biomed. Opt. Express 10(4), 1601–1612 (2019)
Lu, H., Boonarpha, N., Kwong, M.T., Zheng, Y.: Automated segmentation of the choroid in retinal optical coherence tomography images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5869–5872. IEEE (2013)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: IEEE CVPR, pp. 1–8 (2008)
Masood, S., Fang, R., Li, P., Li, H., Sheng, B., Mathavan, A., Wang, X., Yang, P., Wu, Q., Qin, J., et al.: Automatic choroid layer segmentation from optical coherence tomography images using deep learning. Sci. Rep. 9(1), 1–18 (2019)
Masood, S., Sheng, B., Li, P., Shen, R., Fang, R., Wu, Q.: Automatic choroid layer segmentation using normalized graph cut. IET Image Proc. 12(1), 53–59 (2018)
Minhas, S., Javed, M.Y.: Iris feature extraction using gabor filter. In: 2009 International Conference on Emerging Technologies, pp. 252–255. IEEE (2009)
Niu, S., de Sisternes, L., Chen, Q., Leng, T., Rubin, D.L.: Automated geographic atrophy segmentation for sd-oct images using region-based cv model via local similarity factor. Biomed. Opt. Express 7(2), 581–600 (2016)
Novosel, J., Thepass, G., Lemij, H.G., de Boer, J.F., Vermeer, K.A., van Vliet, L.J.: Loosely coupled level sets for simultaneous 3d retinal layer segmentation in optical coherence tomography. Med. Image Anal. 26(1), 146–158 (2015)
Oliveira, J., Pereira, S., Gonçalves, L., Ferreira, M., Silva, C.A.: Multi-surface segmentation of oct images with amd using sparse high order potentials. Biomed. Opt. Express 8(1), 281–297 (2017)
Pekala, M., Joshi, N., Freund, D.E., Bressler, N.M., DeBuc, D.C., Burlina, P.M.: Deep learning based retinal OCT segmentation. CoRR arXiv:1801.09749, 1–11 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234–241 (2015)
Roy, A.G., Conjeti, S., Karri, S.P.K., Sheet, D., Katouzian, A., Wachinger, C., Navab, N.: Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627–3642 (2017)
Sara, U., Akter, M., Uddin, M.S.: Image quality assessment through fsim, ssim, mse and psnr-a comparative study. J. Comput. Commun. 7(3), 8–18 (2019)
Sui, X., Zheng, Y., Wei, B., Bi, H., Wu, J., Pan, X., Yin, Y., Zhang, S.: Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing 237, 332–341 (2017)
Tian, J., Marziliano, P., Baskaran, M., Tun, T.A., Aung, T.: Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images. Biomed. Opt. Express 4(3), 397–411 (2013)
Wang, C., Wang, Y.X., Li, Y.: Automatic choroidal layer segmentation using markov random field and level set method. IEEE J. Biomed. Health Inform. 21(6), 1694–1702 (2017)
Zhang, H., Yang, J., Zhou, K., Li, F., Hu, Y., Zhao, Y., Zheng, C., Zhang, X., Liu, J.: Automatic segmentation and visualization of choroid in oct with knowledge infused deep learning. IEEE J. Biomed. Health Inform. 24(12), 3408–3420 (2020)
Acknowledgements
The authors would like to thank the editors and anonymous reviewers for their insightful comments and suggestions. This work was supported in part by the National Natural Science Foundation of China under Grants 62272298 and 62077037, in part by the National Key Research and Development Program of China under Grant 2022YFC2407000, in part by the Interdisciplinary Program of Shanghai Jiao Tong University under Grants YG2023LC11 and YG2022ZD007, in part by the Medical-industrial Cross-fund of Shanghai Jiao Tong University under Grant YG2022QN089, and in part by The Hong Kong Polytechnic University under Grants P0042740, P0035358, P0030419, P0043906, and P0044520.
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Masood, S., Ali, S.G., Wang, X. et al. Deep choroid layer segmentation using hybrid features extraction from OCT images. Vis Comput 40, 2775–2792 (2024). https://doi.org/10.1007/s00371-023-02985-w
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DOI: https://doi.org/10.1007/s00371-023-02985-w