Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds

Chen X, Ravikumar N, Xia Y, Attar R, Diaz-Pinto A, Piechnik SK, Neubauer S, Petersen SE, Frangi AF (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Medical Image Analysis Elsevier

Book Volume: 74

Article Number: 102228

DOI: 10.1016/j.media.2021.102228

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.

Involved external institutions

University of Leeds GB United Kingdom (GB) University of Oxford GB United Kingdom (GB) Queen Mary University of London GB United Kingdom (GB)

How to cite

APA:

Chen, X., Ravikumar, N., Xia, Y., Attar, R., Diaz-Pinto, A., Piechnik, S.K.,... Frangi, A.F. (2021). Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds. Medical Image Analysis, 74. https://doi.org/10.1016/j.media.2021.102228

MLA:

Chen, Xiang, et al. "Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds." Medical Image Analysis 74 (2021).

BibTeX: Download