Unsupervised single-source domain generalization for robust quantification of lymphatic perfusion
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Infants and young children with Fontan circulation, resulting from lifesaving heart surgery, experience altered hemodynamics that can lead to severe, life-threatening complications, often driven by congestion in the lymphatic system. These complications typically become more apparent as the patients grow older. Early detection and management of these complications require precise assessment of lymphatic perfusion through segmentation of lymphatic fluids. However, manual evaluation is time-intensive and subject to variability, underscoring the need for automated, reliable segmentation in clinical practice. Yet, domain shifts arising from variations in MRI acquisition protocols (e.g., BLADE versus SPACE sequences) and scanner differences present significant challenges for current segmentation models. In this work, we propose the use of causalityinspired single-source domain generalization (CISDG) to develop a robust and accurate segmentation network for lymphatic perfusion patterns across diverse imaging domains. Using a dataset of T2-weighted MR images from 71 patients, we demonstrate that our CISDG model outperforms conventional segmentation networks, including nnU-Net, in both source and target domains. Our results indicate that the proposed method not only generalizes effectively across domain shifts but also holds promise for enhancing diagnostic efficiency and reliability in clinical settings.
Date Issued
2025-03-02
Date Acceptance
2025-03-01
Citation
Bildverarbeitung für die Medizin, 2025, pp.178-184
ISBN
9783658474218
ISSN
1431-472X
Publisher
Springer Fachmedien Wiesbaden
Start Page
178
End Page
184
Journal / Book Title
Bildverarbeitung für die Medizin
Copyright Statement
© 2025 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature. Copyright © YYYY Copyright Owner. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
German Conference on Medical Image Computing
Publication Status
Published
Start Date
2025-03-09
Finish Date
2025-03-11
Coverage Spatial
Regensburg, Germany