CineMA: A Foundation Model for Cine Cardiac MRI

• URL: http://arxiv.org/abs/2506.00679v1
• Date: Sat, 31 May 2025 19:12:34 GMT
• Title: CineMA: A Foundation Model for Cine Cardiac MRI
• Authors: Yunguan Fu, Weixi Yi, Charlotte Manisty, Anish N Bhuva, Thomas A Treibel, James C Moon, Matthew J Clarkson, Rhodri Huw Davies, Yipeng Hu,
• Abstract summary: We developed CineMA, a foundation AI model automating clinically important measurements with limited labels.<n>CineMA is a self-consuming autoencoder model trained on 74,916 cine CMR studies to reconstruct images from masked inputs.<n>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.
• Score: 5.568379273240117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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