Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset.
@article{arxiv.2409.12333,
title = {Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation},
author = {Amine Sadikine and Bogdan Badic and Jean-Pierre Tasu and Vincent Noblet and Pascal Ballet and Dimitris Visvikis and Pierre-Henri Conze},
journal= {arXiv preprint arXiv:2409.12333},
year = {2024}
}