Abstract
The optical coherence tomography (OCT) signs of nascent geographic atrophy (nGA) are highly associated with GA onset. Automatically localizing nGA lesions can assist patient screening and endpoint evaluation in clinical trials. This task can be achieved with supervised object detection models, but they require laborious bounding box annotations. This study thus evaluated whether a weakly supervised method could localize nGA lesions based on the saliency map generated from a deep learning nGA classification model. This multi-instance deep learning model is based on 2D ResNet with late fusion and was trained to classify nGA on OCT volumes. The proposed method was cross-validated using a dataset consisting of 1884 volumes from 280 eyes of 140 subjects, which had volume-wise nGA labels and expert-graded slice-wise lesion bounding box annotations. The area under Precision-Recall curve (AUPRC) or correctly localized lesions was 0.72(±0.08), compared to 0.77(±0.07) from a fully supervised method with YOLO V3. No statistically significant difference is observed between the weakly supervised and fully supervised methods (Wilcoxon signed-rank test, p = 1.0).
H. Yao and A. Pely—Contributed equally to this work.
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Acknowledgements
We would like to thank all of the study participants and their families, and all of the site investigators, study coordinators, and staff. We also appreciate the analysis support from biostatistician Ling Ma.
Funding
Supported by the National Health and Medical Research Council of Australia (project grant no.: APP1027624 [R.H.G.], and fellowship grant nos.: GNT1103013 [R.H.G.], #2008382 [Z.W.]; the BUPA Health Foundation (Australia) (R.H.G.) and the Macular Disease Foundation Australia (Z.W. and R.H.G.). The Centre for Eye Research Australia receives operational infrastructure support from the Victorian Government. Ellex R&D Pty Ltd (Adelaide, Australia) provided partial funding of the central coordinating center and the in-kind provision the Macular Integrity Assessment microperimeters for the duration of the LEAD study. The web-based Research Electronic Data Capture application and open- source platform OpenClinica allowed secure electronic data capture. The LEAD study was sponsored by the Centre for Eye Research Australia, East Melbourne, Australia, an independent medical research institute and a not-for-profit company. This sub-study was supported by Genentech, Inc.
Heming Yao, Adam Pely, Simon S. Gao, Hao Chen, Mohsen Hejrati, and Miao Zhang, are employees of Genentech, Inc. and shareholders in F. Hoffmann La Roche, Ltd.
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Yao, H. et al. (2023). Weakly Supervised Lesion Localization of Nascent Geographic Atrophy in Age-Related Macular Degeneration. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_46
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