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Robust 3D U-Net Segmentation of Macular Holes

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

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Maged Habib

Caspar Geenen

David H. Steel

Boguslaw Obara


Arjun Pakrashi

Ellen Rushe

Mehran Hossein Zadeh Bazargani

Brian Mac Namee


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.


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
Publisher URL


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