标题： 全尺度表示引导网络用于视网膜血管分割 显示英文标题

标题： Full-scale Representation Guided Network for Retinal Vessel Segmentation

摘要： U-Net架构及其变体在过去十年中一直是视网膜血管分割的最先进方法（SOTA）。 在本研究中，我们引入了一个全尺度引导网络（FSG-Net），其中具有现代化卷积块的特征表示网络提取全尺度信息，而引导卷积块则精炼这些信息。 在解释为过滤器行为类似于未锐化掩模过滤器的情况下，引入了注意力引导过滤器到引导卷积块中。 将全尺度信息传递到注意力块允许生成改进的注意力图，然后传递到注意力引导过滤器，从而提高分割网络的性能。 引导卷积块之前的结构可以替换为任何U-Net变体，这增强了所提出方法的可扩展性。 为了公平比较，我们重新实现了公开存储库中最近的研究，以评估其可扩展性和可重复性。 我们的实验还表明，与当前最先进的模型相比，所提出的网络在各种公共数据集上表现出具有竞争力的结果。 消融研究证明，所提出的模型在参数规模较小的情况下仍具有竞争力。 最后，通过将所提出的模型应用于面部皱纹分割，我们确认了其在其他领域类似任务中的可扩展性潜力。 我们的代码可在https://github.com/ZombaSY/FSG-Net-pytorch上获得。 显示英文摘要

摘要： The U-Net architecture and its variants have remained state-of-the-art (SOTA) for retinal vessel segmentation over the past decade. In this study, we introduce a Full Scale Guided Network (FSG-Net), where the feature representation network with modernized convolution blocks extracts full-scale information and the guided convolution block refines that information. Attention-guided filter is introduced to the guided convolution block under the interpretation that the filter behaves like the unsharp mask filter. Passing full-scale information to the attention block allows for the generation of improved attention maps, which are then passed to the attention-guided filter, resulting in performance enhancement of the segmentation network. The structure preceding the guided convolution block can be replaced by any U-Net variant, which enhances the scalability of the proposed approach. For a fair comparison, we re-implemented recent studies available in public repositories to evaluate their scalability and reproducibility. Our experiments also show that the proposed network demonstrates competitive results compared to current SOTA models on various public datasets. Ablation studies demonstrate that the proposed model is competitive with much smaller parameter sizes. Lastly, by applying the proposed model to facial wrinkle segmentation, we confirmed the potential for scalability to similar tasks in other domains. Our code is available on https://github.com/ZombaSY/FSG-Net-pytorch.