Jonathan Frawley jonathan.frawley@durham.ac.uk
PGR Student Doctor of Philosophy
Robust 3D U-Net Segmentation of Macular Holes
Frawley, Jonathan; Willcocks, Chris G.; Habib, Maged; Geenen, Caspar; Steel, David H.; Obara, Boguslaw
Authors
Dr Chris Willcocks christopher.g.willcocks@durham.ac.uk
Associate Professor
Maged Habib
Caspar Geenen
David H. Steel
Boguslaw Obara
Contributors
Arjun Pakrashi
Editor
Ellen Rushe
Editor
Mehran Hossein Zadeh Bazargani
Editor
Brian Mac Namee
Editor
Abstract
Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with a number of design variants. Manually annotating and measuring macular holes is time consuming and error prone, taking dozens of minutes to annotate a single 3D scan. Previous automated approaches to macular hole segmentation take minutes to segment a single 3D scan. We found that, in less than one second, deep learning models generate significantly more accurate segmentations than previous automated approaches (Jaccard index boost of 0.08 − 0.09) and expert agreement (Jaccard index boost of 0.13 − 0.20). We also demonstrate that an approach of architectural simplification, by greatly simplifying the network capacity and depth, results in a model which is competitive with state-of-the-art models such as residual 3D U-Nets.
Citation
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2021). Robust 3D U-Net Segmentation of Macular Holes. In A. Pakrashi, E. Rushe, M. H. Z. Bazargani, & B. Mac Namee (Eds.),
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, Dublin, Republic of Ireland, December 9-10, 2021 |
Start Date | Dec 9, 2021 |
End Date | Dec 10, 2021 |
Acceptance Date | Nov 22, 2021 |
Online Publication Date | Dec 8, 2021 |
Publication Date | 2021 |
Deposit Date | Oct 23, 2022 |
Publicly Available Date | Oct 24, 2022 |
Volume | 3105 |
Pages | 36-47 |
Series Title | CEUR Workshop Proceedings |
Public URL | https://durham-repository.worktribe.com/output/1135252 |
Publisher URL | http://ceur-ws.org/Vol-3105/ |
Files
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
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