期刊
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
卷 11, 期 2, 页码 -出版社
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/tvst.11.2.6
关键词
polypoidal choroidal vasculopathy; fluorescein angiography; deep learning; screening; segmentation
资金
- Ministry of Science and Technology, Taiwan [MOST 105-2221-E-194-049]
- PSC-CUNY Research Award [64450-00-52]
This study developed attention-based deep learning networks for the accurate diagnosis and segmentation of polypoidal choroidal vasculopathy (PCV) using fluorescein angiography (FA). The developed system achieved high classification and segmentation performance, providing a promising tool for early diagnosis and treatment of PCV.
Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. Results: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validated with 56 PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. Conclusions: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. Translational Relevance: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis.
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