Deep vessel segmentation based on a new combination of vesselness
  filters
Guillaume Garret Antoine Vacavant Carole Frindel
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.