ArticleStatistical atlases and computational models of the heart. STACOM (Workshop)2022
Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation.
Article in Statistical atlases and computational models of the heart. STACOM (Workshop), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Efficacy of injectable platelet-rich fibrin and cross-linked hyaluronic acid for augmentation of interdental papilla in terms of true and surrogate end points.Journal of Indian Society of PeriodontologyArticle
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9 authors.
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Abstract
2D cardiac MR cine images provide data with a high signal-to-noise ratio for the segmentation and reconstruction of the heart. These images are frequently used in clinical practice and research. However, the segments have low resolution in the through-plane direction, and standard interpolation methods are unable to improve resolution and precision. We proposed an end-to-end pipeline for producing high-resolution segments from 2D MR images. This pipeline utilised a bilateral optical flow warping method to recover images in the through-plane direction, while a SegResNet automatically generated segments of the left and right ventricles. A multi-modal latent-space self-alignment network was implemented to guarantee that the segments maintain an anatomical prior derived from unpaired 3D high-resolution CT scans. On 3D MR angiograms, the trained pipeline produced high-resolution segments that preserve an anatomical prior derived from patients with various cardiovascular diseases.
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