Abstract
Automated segmentation of the choroid in optical coherence tomography (OCT) images is important to assess diseases which accompany choroidal changes. Existing methods try to segment the boundaries of the choroid by utilizing the edge information. However, in many cases, these boundaries may not have distinct edges thereby rendering edge-based segmentation inaccurate. This paper addresses this issue by performing a region-based segmentation of the choroid instead of an edge-based segmentation. The proposed method uses a deep-learning architecture called U-Net which utilizes the texture of the choroid to segment it. The proposed method was evaluated on a dataset of 1280 OCT images and achieves an intersection over union (IoU) of 0.85. This was better than the IoU of 0.51 achieved by a related work which uses edge-based segmentation. In addition, we have experimentally assessed the effect of removing the retinal layers before choroid segmentation which was performed in the proposed work. Results show that if the retinal layers were not removed, the IoU drops to 0.81. The proposed method can help in automatic analysis of choroidal changes even when the choroidal boundaries are not clear which is often the case in diseased eyes.
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Srivastava, R., Ong, E.P., Lee, BH. (2021). Choroid Segmentation in Optical Coherence Tomography Images Using Deep Learning. In: Lim, C.T., Leo, H.L., Yeow, R. (eds) 17th International Conference on Biomedical Engineering. ICBME 2019. IFMBE Proceedings, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-030-62045-5_4
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DOI: https://doi.org/10.1007/978-3-030-62045-5_4
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