Universal Wavelet Units in 3D Retinal Layer Segmentation
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Abstract (arXiv)
This paper presents the first study to apply tunable wavelet units (UwUs) for 3D retinal layer segmentation from Optical Coherence Tomography (OCT) volumes. To overcome the limitations of conventional max-pooling, we integrate three wavelet-based downsampling modules, OrthLattUwU, BiorthLattUwU, and LS-BiorthLattUwU, into a motion-corrected MGU-Net architecture. These modules use learnable lattice filter banks to preserve both low- and high-frequency features, enhancing spatial detail and structural consistency. Evaluated on the Jacobs Retina Center (JRC) OCT dataset, our framework shows significant improvement in accuracy and Dice score, particularly with LS-BiorthLattUwU, highlighting the benefits of tunable wavelet filters in volumetric medical image segmentation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 22 Jul 2025.
Builds on
5 of 22 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Graph-Based Global Reasoning Networkscited by 120
- 2017ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Networkcited by 37
- 2021Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT imagescited by 14
- 2014Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularizationcited by 2
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Citing papers per year: 2026: 1.
1 paper.