Universal Wavelet Units in 3D Retinal Layer Segmentation
It addresses the problem of precise medical image segmentation for ophthalmology, offering incremental advances over existing methods.
This paper tackled 3D retinal layer segmentation from OCT volumes by integrating tunable wavelet units into a neural network, resulting in significant improvements in accuracy and Dice scores on the JRC dataset.
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.