中文标题#
三維視網膜層分割中的通用小波單元
英文标题#
Universal Wavelet Units in 3D Retinal Layer Segmentation
中文摘要#
本文首次研究將可調小波單元（UwUs）應用於光學相干斷層掃描（OCT）體積的 3D 視網膜層分割。為克服傳統最大池化的局限性，我們將三個基於小波的下採樣模塊 OrthLattUwU、BiorthLattUwU 和 LS-BiorthLattUwU 集成到運動校正的 MGU-Net 架構中。這些模塊使用可學習的格子濾波器組來保留低頻和高頻特徵，從而增強空間細節和結構一致性。在雅各布視網膜中心（JRC）OCT 數據集上進行評估，我們的框架在準確性和 Dice 分數方面表現出顯著提升，特別是 LS-BiorthLattUwU，突顯了可調小波濾波器在體積醫學圖像分割中的優勢。
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.
PDF 获取#
抖音掃碼查看更多精彩內容