Abstract
Segmentation of retinal vessels is of great importance in the diagnosis of eye-related diseases. Many learning-based methods have been proposed for this task and get encouraging results. In this paper, we propose a novel end-to-end Cascaded Attention Guided Network (CAG-Net) for retinal vessel segmentation, which can generate more accurate results for retinal vessel segmentation. Our CAG-Net is a two-step deep neural network which contains two modules, the prediction module and the refinement module. The prediction module is responsible for generating an initial segmentation map, while the refinement module aims at improving the initial segmentation map. The final segmentation result is obtained by integrating the outputs of the two modules. Both of the two modules adopt an Attention UNet++ (AU-Net++) to boost the performance, which employs Attention guided Convolutional blocks (AC blocks) on the decoder. The experimental results show that our proposed network achieved state-of-the-art performance on the three public retinal datasets DRIVE, CHASE_DB1 and STARE.
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Acknowledgement
This work was supported in part by Anhui Provincial Natural Science Foundation under Grant 1908085QF256 and the Fundamental Research Funds for the Central Universities under Grant WK2380000002.
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Li, M., Zhang, Y., Xiong, Z., Liu, D. (2020). Cascaded Attention Guided Network for Retinal Vessel Segmentation. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_7
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