$\Upsilon$-Net: A Spatiospectral Network for Retinal OCT Segmentation
- URL: http://arxiv.org/abs/2204.07613v1
- Date: Fri, 15 Apr 2022 18:51:28 GMT
- Title: $\Upsilon$-Net: A Spatiospectral Network for Retinal OCT Segmentation
- Authors: Azade Farshad, Yousef Yeganeh, Peter Gehlbach, Nassir Navab
- Abstract summary: We present $Upsilon$-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images.
Our improvement was 13% on the fluid segmentation dice score and 1.9% on the average dice score.
- Score: 45.84236875366677
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automated segmentation of retinal optical coherence tomography (OCT) images
has become an important recent direction in machine learning for medical
applications. We hypothesize that the anatomic structure of layers and their
high-frequency variation in OCT images make retinal OCT a fitting choice for
extracting spectral-domain features and combining them with spatial domain
features. In this work, we present $\Upsilon$-Net, an architecture that
combines the frequency domain features with the image domain to improve the
segmentation performance of OCT images. The results of this work demonstrate
that the introduction of two branches, one for spectral and one for spatial
domain features, brings a very significant improvement in fluid segmentation
performance and allows outperformance as compared to the well-known U-Net
model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on
the average dice score. Finally, removing selected frequency ranges in the
spectral domain demonstrates the impact of these features on the fluid
segmentation outperformance.
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