Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Cardiac CINE MRI, unsupervised learning, cardiac function assessment, spatial and temporal representations
Motivation: CMR is the golden standard for cardiac diagnosis, and medical data annotation is time-consuming. Thus, screening techniques from unlabeled data can help streamline the cardiac diagnosis process.
Goal(s): This work aims to enable cardiac function assessment from unlabeled cardiac MR images using an unsupervised approach with masked image modeling.
Approach: Our model creates a robust latent space by reconstructing sparse 2D+T planes (SAX, 2CH, 3CH, and 4CH views) with 70% masking, which can be further disentangled into distinct cardiac temporal states.
Results: t-SNE visualization and kNN clustering analysis confirm the association between latent space and cardiac phenotypes, highlighting strong temporal feature extraction.
Impact: This method offers a scalable approach for cardiac screening by creating a latent space as well as distinct time-segment embeddings, enabling diverse preliminary analysis of cardiac function and potentially advancing research in cardiovascular disease applications.
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