A study of cross-dataset generalization on liver vessel segmentation improved by a topological loss.

Authors

Jose M Saavedra, Héctor Henríquez, Miguel Chicchon, Camila Figueroa, Marcelo Pizarro, Joaquín Curimil, Violeta Chang

Published in

Medical & biological engineering & computing. Jul 21, 2026. Epub Jul 21, 2026.

Abstract

Our research addresses the critical task of localizing liver vascularity for medical applications such as surgical planning and intraoperative navigation. We conduct a comprehensive and unbiased evaluation of contemporary segmentation architectures for liver vessel segmentation, comparing UNet-based, mask-based, and foundational models. Our analysis emphasizes cross-dataset generalization by assessing model performance on multiple datasets. Notably, we achieve strong generalization by integrating region-based and topologically based Dice loss functions. This approach substantially improves cross-domain generalization, yielding clDice scores of 0.7132 on the IRCAD dataset and 0.6704 on the MSD dataset, even when these datasets were not included in the training set. Additionally, training with a mixed dataset further increases the MSD Dice score to 0.7472.

PMID:
42479323
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.