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Segmentation of Macular Edema Datasets with Small Residual 3D U-Net Architectures

Lookup NU author(s): Dr Chris Willcocks, Maged Habib, Professor David SteelORCiD, Professor Boguslaw ObaraORCiD


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© 2020 IEEE. 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.

Publication metadata

Author(s): Frawley J, Willcocks CG, Habib M, Geenen C, Steel DH, Obara B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 20th International Conference on Bioinformatics and Bioengineering (BIBE 2020)

Year of Conference: 2020

Pages: 582-587

Online publication date: 16/12/2020

Acceptance date: 02/04/2018

ISSN: 2471-7819

Publisher: IEEE


DOI: 10.1109/BIBE50027.2020.00100

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728195742