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.
@article{arxiv.2402.14509,
title = {Deep vessel segmentation based on a new combination of vesselness filters},
author = {Guillaume Garret and Antoine Vacavant and Carole Frindel},
journal= {arXiv preprint arXiv:2402.14509},
year = {2024}
}
Comments
8 pages, 3 figures. This work has been submitted to the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024) for possible publication