Abstract
Corneal ulcer segmentation from fluorescein staining images is vital for objective and quantitative assessments of ocular surface damages. How to utilize prior information from the fluorescein staining images is a challenge. In this work, we propose and validate a novel method for corneal ulcer segmentation. Leveraging Adjacent Scale Fusion and Corneal Position Embedding, our method can effectively capture fine patterns of the corneal ulcer as well as explicitly characterize the discriminating relative position information within the cornea. We evaluate the corneal ulcer segmentation performance of our method on a publicly-accessible SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers, with a mean Dice similarity coefficient of 80.73% and a mean Jaccard Index of 71.63% having been obtained. Quantitative results identify the superiority of the proposed method over representative state-of-the-art deep learning frameworks. In addition, the importance of each key component in the proposed method is analyzed both quantitatively and qualitatively.
Z. Wang and J. Lyu contributed equally to this work.
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Wang, Z., Lyu, J., Luo, W., Tang, X. (2021). Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_1
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