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• W4317832372 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." @default.
• W4317832372 created "2023-01-24" @default.
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• W4317832372 date "2023-04-18" @default.
• W4317832372 modified "2023-09-27" @default.
• W4317832372 title "Scale-Specific Auxiliary Multi-Task Contrastive Learning for Deep Liver Vessel Segmentation" @default.
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• W4317832372 doi "https://doi.org/10.1109/isbi53787.2023.10230364" @default.
• W4317832372 hasPublicationYear "2023" @default.
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