Abstract
Detecting the high-intensity retinal leakage in fundus fluorescein angiography (FA) image is a key step for retinal-related disease diagnosis and treatment. In this study, we proposed an unsupervised learning-based fluorescein leakage detecting method which can give the leakage detection results without the need for manual annotation. In this method, a model that can generate the normal-looking FA image from the input abnormal FA image is trained; and then the leakage can be detected by making the difference between the abnormal and generated normal image. The proposed method was validated on the publicly available datasets, and qualitatively and quantitatively compared with the state-of-the-art leakage detection methods. The comparison results indicate that the proposed method has higher accuracy in leakage detection, and can detect an image in a very short time (in 1 s), which has great potential significance for clinical diagnosis.
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Appendix: Failure Detection Examples of the Proposed Method
Appendix: Failure Detection Examples of the Proposed Method
See Fig. 6.
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Li, W., He, Y., Wang, J., Kong, W., Chen, Y., Shi, G. (2020). An Unsupervised Adversarial Learning Approach to Fundus Fluorescein Angiography Image Synthesis for Leakage Detection. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_15
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DOI: https://doi.org/10.1007/978-3-030-59520-3_15
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