Vascular segmentation represents a crucial clinical task, yet its automation remains challenging. Because of the recent strides in deep learning, vesselness filters, which can significantly aid the learning process, have been overlooked. This study introduces an innovative filter fusion method crafted to amplify the effectiveness of vessel segmentation models. Our investigation seeks to establish the merits of a filter-based learning approach through a comparative analysis. Specifically, we contrast the performance of a U-Net model trained on CT images with an identical U-Net configuration trained on vesselness hyper-volumes using matching parameters. Our findings, based on two vascular datasets, highlight improved segmentations, especially for small vessels, when the model's learning is exposed to vessel-enhanced inputs.

翻译：血管分割是一项关键的临床任务，但其自动化仍面临挑战。由于深度学习的近期进展，能够显著辅助学习过程的血管性过滤器被忽视了。本研究引入了一种创新的滤波器融合方法，旨在增强血管分割模型的有效性。我们通过对比分析来确立基于滤波器的学习方法的价值。具体而言，我们对比了在CT图像上训练的U-Net模型与使用匹配参数在血管性超体积上训练的相同U-Net配置的性能。基于两个血管数据集的研究结果表明，当模型学习接触到血管增强输入时，分割效果得到改善，尤其是对于小血管而言。