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

doi:10.1109/bibe50027.2020.00100

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

Authors

Jonathan Frawley

Dr Chris Willcocks christopher.g.willcocks@durham.ac.uk
Associate Professor

Maged Habib

Caspar Geenen

David H.W. Steel

Boguslaw Obara

Abstract

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.

Citation

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. . https://doi.org/10.1109/bibe50027.2020.00100

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
DOI	https://doi.org/10.1109/bibe50027.2020.00100

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