Unsupervised whole-heart function assessment
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915606002925568 |
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| author | Zhang, Yundi Rueckert, Daniel Pan, Jiazhen |
| author_facet | Zhang, Yundi Rueckert, Daniel Pan, Jiazhen |
| contents | 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: 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised whole-heart function assessment Zhang, Yundi Rueckert, Daniel Pan, Jiazhen Tissues and Organs 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: 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. |
| title | Unsupervised whole-heart function assessment |
| topic | Tissues and Organs |
| url | https://arxiv.org/abs/2511.05587 |