Deng, Y;
Wen, Y;
Qian, L;
Puyol Anton, E;
Xu, H;
Pushparajah, K;
Ibrahim, Z;
... Young, A; + view all
(2023)
Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation.
In: Camara, O and Puyol-Anton, E and Qin, C and Sermesant, M and Suinesiaputra, A and Wang, S and Young, A, (eds.)
Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers.
(pp. pp. 26-35).
Springer: Cham, Switzerland.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation |
| Event: | 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022 |
| Location: | SINGAPORE, Singapore |
| Dates: | 18 Sep 2022 |
| ISBN-13: | 9783031234422 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-23443-9_3 |
| Publisher version: | https://doi.org/10.1007/978-3-031-23443-9_3 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Technology, Cardiac & Cardiovascular Systems, Computer Science, Interdisciplinary Applications, Mathematical & Computational Biology, Cardiovascular System & Cardiology, Computer Science, Super-resolution segmentation, Domain adaptation, Cardiac MR, CT angiogram, VAE, ENHANCEMENT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204637 |
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