CineMA: A Foundation Model for Cine Cardiac MRI
Journal:
arXiv
Published Date:
May 31, 2025
Abstract
Cardiac magnetic resonance (CMR) is a key investigation in clinical
cardiovascular medicine and has been used extensively in population research.
However, extracting clinically important measurements such as ejection fraction
for diagnosing cardiovascular diseases remains time-consuming and subjective.
We developed CineMA, a foundation AI model automating these tasks with limited
labels. CineMA is a self-supervised autoencoder model trained on 74,916 cine
CMR studies to reconstruct images from masked inputs. After fine-tuning, it was
evaluated across eight datasets on 23 tasks from four categories: ventricle and
myocardium segmentation, left and right ventricle ejection fraction
calculation, disease detection and classification, and landmark localisation.
CineMA is the first foundation model for cine CMR to match or outperform
convolutional neural networks (CNNs). CineMA demonstrated greater label
efficiency than CNNs, achieving comparable or better performance with fewer
annotations. This reduces the burden of clinician labelling and supports
replacing task-specific training with fine-tuning foundation models in future
cardiac imaging applications. Models and code for pre-training and fine-tuning
are available at https://github.com/mathpluscode/CineMA, democratising access
to high-performance models that otherwise require substantial computational
resources, promoting reproducibility and accelerating clinical translation.
