Deep vessel segmentation based on a new combination of vesselness filters
This work addresses the problem of accurate vessel segmentation for clinical applications, but it is incremental as it builds on existing U-Net models by integrating vesselness filters.
The study tackled the challenge of automating vascular segmentation by introducing a filter fusion method that enhances vesselness filters to improve deep learning models. The result showed improved segmentations, particularly for small vessels, when using vessel-enhanced inputs compared to standard CT images.
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.