Chen, X orcid.org/0000-0003-4203-4578, Ravikumar, N, Xia, Y et al. (6 more authors) (2021) Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds. Medical Image Analysis, 74. 102228. ISSN 1361-8415
Abstract
Shape reconstruction from sparse point clouds/images is a challenging and relevant task required for a va- riety of applications in computer vision and medical image analysis (e.g. surgical navigation, cardiac mo- tion 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 in- volving 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 tech- niques 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 spa- tial resolution is ∼1 . 8 ×1 . 8 ×10 mm 3 ). 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
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | ©2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ ) |
Keywords: | Cardiac mesh reconstruction; Graph convolutional network; Deep learning; Contours to mesh reconstruction; Cardiac surface reconstruction |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2021 14:05 |
Last Modified: | 19 Oct 2021 14:05 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2021.102228 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179372 |