中文标题#
三维视网膜层分割中的通用小波单元
英文标题#
Universal Wavelet Units in 3D Retinal Layer Segmentation
中文摘要#
本文首次研究将可调小波单元（UwUs）应用于光学相干断层扫描（OCT）体积的 3D 视网膜层分割。为克服传统最大池化的局限性，我们将三个基于小波的下采样模块 OrthLattUwU、BiorthLattUwU 和 LS-BiorthLattUwU 集成到运动校正的 MGU-Net 架构中。这些模块使用可学习的格子滤波器组来保留低频和高频特征，从而增强空间细节和结构一致性。在雅各布视网膜中心（JRC）OCT 数据集上进行评估，我们的框架在准确性和 Dice 分数方面表现出显著提升，特别是 LS-BiorthLattUwU，突显了可调小波滤波器在体积医学图像分割中的优势。
英文摘要#
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.
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