Abstract
Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framework to effectively classify glaucoma OCT images from normal ones. A semi-supervised learning strategy with smoothness assumption is applied for surrogate assignment of missing function regression labels. Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting. Essentially, we are the first to unify the structure analysis and function regression for glaucoma screening. It is also worth noting that we build the largest glaucoma OCT image dataset involving 4877 volumes to develop and evaluate the proposed method. Extensive experiments demonstrate that our framework outperforms the baseline methods and two glaucoma experts by a large margin, achieving 93.2%, 93.2% and 97.8% on accuracy, F1 score and AUC, respectively.
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Acknowledgements
This project is supported in part by the National Basic Program of China 973 Program under Grant 2015CB351706, grants from the National Natural Science Foundation of China with Project No. U1613219, Research Grants Council - General Research Fund, Hong Kong (Ref: 14102418) and Shenzhen Science and Technology Program (No. JCYJ20180507182410327).
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Wang, X. et al. (2019). Unifying Structure Analysis and Surrogate-Driven Function Regression for Glaucoma OCT Image Screening. 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_5
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DOI: https://doi.org/10.1007/978-3-030-32239-7_5
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