| Title: | Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds |
| Journal: | Medical Image Analysis |
| Published: | 9 Sep 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34563860/ |
| DOI: | https://doi.org/10.1016/j.media.2021.102228 |
Publication 10267
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Abstract
Shape reconstruction from sparse point clouds/images is a challenging and relevant task required for a variety of applications in computer vision and medical image analysis (e.g. surgical navigation, cardiac motion analysis, augmented/virtual reality systems). A subset of such methods, viz. 3D shape reconstruction from 2D contours, is especially relevant for computer-aided diagnosis and intervention applications involving meshes derived from multiple 2D image slices, views or projections. We propose a deep learning architecture, coined Mesh Reconstruction Network (MR-Net), which tackles this problem. MR-Net enables accurate 3D mesh reconstruction in real-time despite missing data and with sparse annotations. Using 3D cardiac shape reconstruction from 2D contours defined on short-axis cardiac magnetic resonance image slices as an exemplar, we demonstrate that our approach consistently outperforms state-of-the-art techniques for shape reconstruction from unstructured point clouds. Our approach can reconstruct 3D cardiac meshes to within 2.5-mm point-to-point error, concerning the ground-truth data (the original image spatial resolution is ∼1.8×1.8×10mm3). We further evaluate the robustness of the proposed approach to incomplete data, and contours estimated using an automatic segmentation algorithm. MR-Net is generic and could reconstruct shapes of other organs, making it compelling as a tool for various applications in medical image analysis.</p>
9 Authors
- Xiang Chen
- Nishant Ravikumar
- Yan Xia
- Rahman Attar
- Andres Diaz-Pinto
- Stefan K Piechnik
- Stefan Neubauer
- Steffen E Petersen
- Alejandro F Frangi
1 Application
| Application ID | Title |
|---|---|
| 11350 | CARDIOMARKER- Computational imaging phenomics in population cardiac MRI with automatic image quality assessment: benchmarking, scalability and inference with state-of-the-art algorithms. |
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