Abstract
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irregularity of FAZ are important biomarkers of various ocular pathologies. Therefore, precise segmentation of FAZ has great clinical interest. Also, there is no existing research reporting that FAZ features can improve the performance of deep diagnostic classification networks. In this paper, we propose a novel multi-level boundary shape and distance aware joint learning framework, named BSDA-Net, for FAZ segmentation and diagnostic classification from OCTA images. Two auxiliary branches, namely boundary heatmap regression and signed distance map reconstruction branches, are constructed in addition to the segmentation branch to improve the segmentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net is found to yield state-of-the-art segmentation and classification results on the OCTA-500, OCTAGON, and FAZID datasets.
L. Lin and Z. Wang—Contributed equally to this work.
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References
Agarwal, A., Balaji, J., Raman, R., Lakshminarayanan, V.: The Foveal avascular zone image database (FAZID). In: Applications of Digital Image Processing XLIII, vol. 11510, pp. 1151027. International Society for Optics and Photonics (2020)
Alam, M., Le, D., Lim, J., Chan, R., Yao, X.: Supervised machine learning based multi-task artificial intelligence classification of retinopathies. J. Clin. Med. 8(6), 872 (2019)
Andreeva, R., Fontanella, A., Giarratano, Y., Bernabeu, M.: DR detection using optical coherence tomography angiography (OCTA): a transfer learning approach with robustness analysis. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) International Workshop on Ophthalmic Medical Image Analysis, vol. 12069, pp. 11–20. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63419-3_2
Balaji, J., Agarwal, A., Raman, R., Lakshminarayanan, V.: Comparison of foveal avascular zone in diabetic retinopathy, high myopia, and normal fundus images. In: Ophthalmic Technologies XXX, vol. 11218, pp. 1121810. International Society for Optics and Photonics (2020)
Chen, L., 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.) Proceedings of the European Conference on Computer Vision (ECCV). LNCS, vol. 11211, pp. 801–818 (2018)
De Carlo, T., Romano, A., Waheed, N., Duker, J.: A review of optical coherence tomography angiography (OCTA). Int. J. Retin. Vitr. 1(1), 5 (2015)
Díaz, M., Novo, J., Cutrín, P., Gómez-Ulla, F., Penedo, M., Ortega, M.: Automatic segmentation of the Foveal avascular zone in ophthalmological OCT-A images. PLoS One 14(2), e0212364 (2019)
Guo, M., Zhao, M., Cheong, A., Dai, H., Lam, A., Zhou, Y.: Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning. Vis. Comput. Ind. Biomed. Art 2(1), 1–9 (2019)
Haddouche, A., Adel, M., Rasigni, M., Conrath, J., Bourennane, S.: Detection of the foveal avascular zone on retinal angiograms using Markov random fields. Digital Sig. Process. 20(1), 149–154 (2010)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heimann, T., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imag. 28(8), 1251–1265 (2009)
Le, D., et al.: Transfer learning for automated OCTA detection of diabetic retinopathy. Transl. Vis. Sci. Technol. 9(2), 35 (2020)
Leitgeb, R.: En face optical coherence tomography: a technology review. Biomed. Opt. Express 10(5), 2177–2201 (2019)
Li, M., et al.: Image projection network: 3D to 2D image segmentation in octa images. IEEE Trans. Med. Imag. 39(11), 3343–3354 (2020)
Li, M., et al.: IPN-V2 and OCTA-500: methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261 (2020)
Li, M., Wang, Y., Ji, Z., Fan, W., Yuan, S., Chen, Q.: Fast and robust fovea detection framework for OCT images based on Foveal avascular zone segmentation. OSA Continuum 3(3), 528–541 (2020)
Linderman, R., Salmon, A., Strampe, M., Russillo, M., Khan, J., Carroll, J.: Assessing the accuracy of Foveal avascular zone measurements using optical coherence tomography angiography: segmentation and scaling. Transl. Vis. Sci. Technol. 6(3), 16 (2017)
Lu, Y., et al.: Evaluation of automatically quantified foveal avascular zone metrics for diagnosis of diabetic retinopathy using optical coherence tomography angiography. Investig. Ophthalmol. Vis. Sci. 59(6), 2212–2221 (2018)
Ma, Y., et al.: ROSE: a retinal OCT-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imag. (2020, in Press)
Mehta, S., et al.: Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. In: In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. MICCAI 2018. LNCS, vol. 11071, pp. 893–901. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_99
Ometto, G., Montesano, G., Chakravarthy, U., Kee, F., Hogg, R., Crabb, D.: Fast 3-dimensional estimation of the foveal avascular zone from OCTA. arXiv preprint arXiv:2012.09945 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Maier-Hein, G.E.B., Fritzsche, K., Deserno, G.E.B., Lehmann, T., Handels, H., Tolxdorff, T. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Salles, M., Kvanta, A., Amrén, U., Epstein, D.: Optical coherence tomography angiography in central retinal vein occlusion: correlation between the foveal avascular zone and visual acuity. Investig. Ophthalmol. Vis. Sci. 57(9), OCT242–OCT246 (2016)
Silva, A., et al.: Segmentation of Foveal avascular zone of the retina based on morphological alternating sequential filtering. In: Proceedings of the IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 38–43 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tan, C., et al.: Deep multi-task and task-specific feature learning network for robust shape preserved organ segmentation. In: IEEE International Symposium on Biomedical Imaging, pp. 1221–1224 (2018)
Wijnen, K., et al.: Automated Lesion detection by regressing intensity-based distance with a neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, pp. 234–242. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_26
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Zhang, H., et al.: ResNeSt: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Zheng, Y., Gandhi, J., Stangos, A., Campa, C., Broadbent, D., Harding, S.: Automated segmentation of foveal avascular zone in Fundus Fluorescein angiography. Investig. Ophthalmol. Vis. Sci. 51(7), 3653–3659 (2010)
Zhou, Z., Siddiquee, M., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imag. 39(6), 1856–1867 (2019)
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Lin, L. et al. (2021). BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_7
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