Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
Cardiac image segmentation is an important step in many cardiac image
analysis and modeling tasks such as motion tracking or simulations of cardiac
mechanics. While deep learning has greatly advanced segmentation in clinical
settings, there is limited work on pre-clinical imaging, notably in porcine
models, which are often used due to their anatomical and physiological
similarity to humans. However, differences between species create a domain
shift that complicates direct model transfer from human to pig data.
Recently, foundation models trained on large human datasets have shown
promise for robust medical image segmentation; yet their applicability to
porcine data remains largely unexplored. In this work, we investigate whether
foundation models can generate sufficiently accurate pseudo-labels for pig
cardiac CT and propose a simple self-training approach to iteratively refine
these labels. Our method requires no manually annotated pig data, relying
instead on iterative updates to improve segmentation quality. We demonstrate
that this self-training process not only enhances segmentation accuracy but
also smooths out temporal inconsistencies across consecutive frames. Although
our results are encouraging, there remains room for improvement, for example by
incorporating more sophisticated self-training strategies and by exploring
additional foundation models and other cardiac imaging technologies.
