RSF-Conv：用于视网膜血管分割的旋转与尺度等变 Fourier 参数化卷积
RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation
AI总结：
针对 CNN 难以刻画视网膜血管旋转与尺度对称性的问题，提出可即插即用且降参数的 RSF-Conv，在域外分割和动静脉分类中展现强泛化性。
摘要：
视网膜血管分割对许多眼部相关疾病的诊断具有重要临床意义，但由于血管形态复杂，它仍是一项艰巨挑战。借助对视网膜血管中平移对称性的出色刻画，卷积神经网络（CNN）已在视网膜血管分割中取得巨大成功。然而，旋转与尺度对称性作为视网膜血管中更普遍的图像先验，却无法由 CNN 刻画。因此，我们提出一种专门用于视网膜血管分割的旋转与尺度等变 Fourier 参数化卷积（RSF-Conv），并给出相应的等变性分析。作为通用模块，RSF-Conv 能够以即插即用的方式集成到现有网络中，同时显著减少参数数量。例如，我们用 RSF-Conv 替换 U-Net 和 Iter-Net 中的传统卷积滤波器，并忠实地开展了全面实验。RSF-Conv+U-Net 和 RSF-Conv+Iter-Net 不仅在域内评估下具有轻微优势，更重要的是，在域外评估下以显著优势优于所有比较方法。这表明 RSF-Conv 具有显著的泛化能力，对于临床实践中普遍存在的跨设备和跨医院挑战具有更大的实际临床意义。为全面证明 RSF-Conv 的有效性，我们还将 RSF-Conv+U-Net 和 RSF-Conv+Iter-Net 应用于视网膜动静脉分类，并取得了有前景的性能，表明其具有临床应用潜力。
英文摘要：
Retinal vessel segmentation is of great clinical significance for the diagnosis of many eye-related diseases, but it is still a formidable challenge due to the intricate vascular morphology. With the skillful characterization of the translation symmetry existing in retinal vessels, convolutional neural networks (CNNs) have achieved great success in retinal vessel segmentation. However, the rotation-and-scale symmetry, as a more widespread image prior in retinal vessels, fails to be characterized by CNNs. Therefore, we propose a rotation-and-scale equivariant Fourier parameterized convolution (RSF-Conv) specifically for retinal vessel segmentation, and provide the corresponding equivariance analysis. As a general module, RSF-Conv can be integrated into existing networks in a plug-and-play manner while significantly reducing the number of parameters. For instance, we replace the traditional convolution filters in U-Net and Iter-Net with RSF-Convs, and faithfully conduct comprehensive experiments. RSF-Conv+U-Net and RSF-Conv+Iter-Net not only have slight advantages under in-domain evaluation, but more importantly, outperform all comparison methods by a significant margin under out-of-domain evaluation. It indicates the remarkable generalization of RSF-Conv, which holds greater practical clinical significance for the prevalent cross-device and cross-hospital challenges in clinical practice. To comprehensively demonstrate the effectiveness of RSF-Conv, we also apply RSF-Conv+U-Net and RSF-Conv+Iter-Net to retinal artery/vein classification and achieve promising performance as well, indicating its clinical application potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
