Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

• URL: http://arxiv.org/abs/2409.12333v1
• Date: Wed, 18 Sep 2024 22:03:22 GMT
• Title: Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
• Authors: Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Pascal Ballet, Dimitris Visvikis, Pierre-Henri Conze,
• Abstract summary: This paper provides a new deep supervised approach for vessel segmentation. We propose a new clustering technique to decompose the tree into various scale levels. 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.
• Score: 0.8713453935346684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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