Abstract
Myopia is a leading cause of visual impairment and blindness in several countries. Effective diagnosis and intervention are crucial, typically relying on manual image analysis by ophthalmologists, which is time-consuming and experience-dependent. In this work, we introduce an ensemble of deep learning techniques for myopic maculopathy plus lesions segmentation. Specifically, we utilize UNet, UNet++, and DeeplabV3+ to segment three lesions with strong data augmentation. Our ensembled model has proved to be effective in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). This dataset covers classification, segmentation, and spherical equivalent prediction, fostering automated analysis research. Extensive experiments on the MMAC dataset reveal the superior performance of our proposed approach, which ranked 1st on the challenge leaderboard. This work addresses a critical need for accurate and efficient myopic maculopathy diagnosis and intervention. The code will be publicly available at https://github.com/FDU-VTS/MMAC.
This work was supported by the National Natural Science Foundation of China (No. 62172101), Chinese National key research and development program (Project number 2021YFC2702100) and Science and Technology Commission of Shanghai Municipality (No. 21511104502).
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Xiao, F. et al. (2024). Ensemble Deep Learning Approaches for Myopic Maculopathy Plus Lesions Segmentation. In: Sheng, B., Chen, H., Wong, T.Y. (eds) Myopic Maculopathy Analysis. MICCAI 2023. Lecture Notes in Computer Science, vol 14563. Springer, Cham. https://doi.org/10.1007/978-3-031-54857-4_4
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