High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network
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Abstract (arXiv)
Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases. Echocardiography-based measurements of LA dimensions and strains are useful biomarkers, but they provide an incomplete picture of atrial deformations. High-resolution dynamic magnetic resonance images (Cine MRI) offer the opportunity to examine LA motion and deformation in 3D, at higher spatial resolution and with full LA coverage. However, there are no dedicated tools to automatically characterise LA motion in 3D. Thus, we propose a tool that automatically segments the LA and extracts the displacement fields across the cardiac cycle. The pipeline is able to accurately track the LA wall across the cardiac cycle with an average Hausdorff distance of 2.51 ± 1.3~mm and Dice score of 0.96 ± 0.02.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 5 Sep 2023.
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5 of 16 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
- 2017Squeeze-and-Excitation Networkscited by 4,772
- 2015Spatial Transformer Networkscited by 1,425
- 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
- 2022Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentationcited by 5
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