Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
<|reference_start|>Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation: 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.<|reference_end|>
@article{sadikine2024scale-specific,
    title={Scale-specific auxiliary multi-task contrastive learning for deep liver
  vessel segmentation},
    author={Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Pascal
  Ballet, Dimitris Visvikis, Pierre-Henri Conze},
    journal={2023 IEEE 20th International Symposium on Biomedical Imaging
  (ISBI), Cartagena, Colombia, 2023, pp. 1-5},
    year={2024},
    doi={10.1109/ISBI53787.2023.10230364},
    archivePrefix={arXiv},
    eprint={2409.12333},
    primaryClass={eess.IV cs.AI cs.CV cs.LG}
}