Abstract
Retinal vessel segmentation is a fundamental step in diagnosis for retinal image analysis. Though many segmentation methods are proposed, little research considers how to ensemble their results to fully exploit the advantages of each method. In this work, we propose a novel unsupervised ensemble strategy to automatically combine multiple segmentation results for an accurate result. There is a no-reference network that could assess the vessel segmentation quality without knowing the ground truth. We then optimize the weight of individual result to maximize this segmentation quality score to enhance the final result. Through extensive experiments, our method has shown superior performance over the state-of-the-art on the DRIVE, STARE, CHASE_DB1 datasets.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61602020.
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Liu, B., Gu, L., Lu, F. (2019). Unsupervised Ensemble Strategy for Retinal Vessel Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_13
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DOI: https://doi.org/10.1007/978-3-030-32239-7_13
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