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arXiv · 2103.01299

Robust 3D U-Net Segmentation of Macular Holes

Jonathan Frawley·Chris G. Willcocks·Maged Habib·Caspar Geenen·David H. Steel·Boguslaw Obara
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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. Previous automated approaches to macular hole segmentation take minutes to segment a single 3D scan. Our proposed model generates significantly more accurate segmentations in less than a second. We found that an approach of architectural simplification, by greatly simplifying the network capacity and depth, exceeds both expert performance and state-of-the-art models such as residual 3D U-Nets.

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Jonathan Frawley, Chris G. Willcocks, Maged Habib, Caspar Geenen, David H. Steel, Boguslaw Obara. 2021-03-01. Robust 3D U-Net Segmentation of Macular Holes. https://arxiv.org/abs/2103.01299

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