用可调小波单元提升OCT眼底层分割精度
Universal Wavelet Units in 3D Retinal Layer Segmentation
- 引入可学习的小波滤波器替代传统池化，保留高低频特征
- 在JRC数据集上Dice分数显著提升，最优模块达0.921
- 适合需要高精度医学图像分割的研究者和临床应用
本文首次将可调小波单元（UwUs）应用于从光学相干断层扫描（OCT）体积中进行3D眼底层分割。为克服传统最大池化方法的局限性，我们将在运动校正的MGU-Net架构中集成三种基于小波的下采样模块：OrthLattUwU、BiorthLattUwU和LS-BiorthLattUwU。这些模块利用可学习的格栅滤波组，有效保留低频与高频特征，增强空间细节与结构一致性。在雅各布视网膜中心（JRC）OCT数据集上的评估表明，该框架在准确率和Dice分数上均有显著提升，尤其是LS-BiorthLattUwU模块表现最佳，验证了可调小波滤波器在三维医学图像分割中的优势。
原文摘要 · Abstract (English)
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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