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.