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Segmentation of macular edema datasets with small residual 3D U-Net architectures

Frawley, Jonathan; Willcocks, Chris G.; Habib, Maged; Geenen, Caspar; Steel, David H.W.; Obara, Boguslaw

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Jonathan Frawley

Maged Habib

Caspar Geenen

David H.W. Steel

Boguslaw Obara


This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find that, contrary to popular belief, neural architectures within this application setting are able to achieve close to human-level performance on unseen test images without requiring large numbers of training examples. Annotating these 3D datasets is difficult, with multiple criteria required. It takes an experienced clinician two days to annotate a single 3D image, whereas our trained model achieves similar performance in less than a second. We found that an approach which uses targeted dataset augmentation, alongside architectural simplification with an emphasis on residual design, has acceptable generalization performance- despite relying on fewer than 15 training examples.


Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020). Segmentation of macular edema datasets with small residual 3D U-Net architectures. .

Conference Name 20th IEEE International Conference on BioInformatics and BioEngineering
Conference Location Cincinnati, OH
Start Date Oct 26, 2020
End Date Oct 28, 2020
Acceptance Date Aug 12, 2020
Online Publication Date Dec 16, 2020
Publication Date 2020
Deposit Date Aug 12, 2020
Publicly Available Date Aug 12, 2020
Publisher Institute of Electrical and Electronics Engineers


Accepted Conference Proceeding (7.2 Mb)

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