Robust 3D U-Net Segmentation of Macular Holes

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

Robust 3D U-Net Segmentation of Macular Holes

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

Authors

Jonathan Frawley jonathan.frawley@durham.ac.uk
PGR Student Doctor of Philosophy

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.),

Conference Name	The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, Dublin, Republic of Ireland, December 9-10, 2021
Conference Location	Dublin, Ireland
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/

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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)