| Title: | Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images |
| Journal: | Medical Image Analysis |
| Published: | 23 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37804586/ |
| DOI: | https://doi.org/10.1016/j.media.2023.102975 |
| Citations: | 7 (7 in last 2 years) as of 8 Aug 2024 |
Publication 9481
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Abstract
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.</p>
4 Authors
- Marcel Beetz
- Abhirup Banerjee
- Julius Ossenberg-Engels
- Vicente Grau
1 Application
| Application ID | Title |
|---|---|
| 40161 | Application of deep learning for the characterisation of motion patterns in the heart |
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