New software developments for quality mesh generation and optimization from biomedical imaging data

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Mei, Xu; Hoshijima, Masahiko

doi:10.1016/j.cmpb.2013.08.009

Cited by 9 publications

(5 citation statements)

References 56 publications

(63 reference statements)

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“…We used the tetgen library (published at http://tetgen.berlios.de/) for creating the tetrahedral elements of the mesh based on CT data. The recent meshing techniques or software can be referred to in [38] and in [39]. The quality and performance were examined using both public domain data and clinical CT data.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Direct volume manipulation for visualizing intraoperative liver resection process

Nakao

Yuya

Taura

et al. 2014

Computer Methods and Programs in Biomedicine

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This paper introduces a new design and application for direct volume manipulation for visualizing the intraoperative liver resection process. So far, interactive volume deformation and resection have been independently handled due to the difficulty of representing elastic behavior of volumetric objects. Our framework models global shape editing and discontinuous local deformation by merging proxy geometry encoding and displacement mapping. A local-frame-based elastic model is presented to allow stable editing of the liver shape including bending and twisting while preserving the volume. Several tests using clinical CT data have confirmed the developed software and interface can represent the intraoperative state of liver and produce local views of reference vascular structures, which provides a "road map of vessels" that are key features when approaching occluded tumors during surgery.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Direct volume manipulation for visualizing intraoperative liver resection process

Nakao

Yuya

Taura

et al. 2014

Computer Methods and Programs in Biomedicine

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“…Octreebased methods recursively subdivide the domain until some predefined stopping criteria are reached. Other methods aimed at increasing the quality of a tetrahedral mesh exist: topology optimisation which modifies the connectivity between mesh vertices while keeping vertex positions unchanged; vertex insertion/deletion inside the mesh; vertex smoothing, which repositions the coordinates of the vertices while keeping the connectivity unchanged [38,39]; variational methods, which account for the intensity of the underlying image to optimise the position of nodes [40,41].…”

Section: Unstructured Meshingmentioning

confidence: 99%

Image-based biomechanical models of the musculoskeletal system

et al. 2020

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Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.

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“…Delaunay mesh generation. There are three main categories of tetrahedral meshing algorithms: (i) advancingfront-based approaches [14,15,16], (ii) octree-based methods [17,18,19], and (iii) Delaunay-based meshing algorithms [20]. A complete review of these algorithms is beyond the scope of this paper.…”

Section: Related Workmentioning

confidence: 99%

Tetrahedral meshing via maximal Poisson-disk sampling

Guo

Yan

Chen

et al. 2016

Computer Aided Geometric Design

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In this paper, we propose a simple yet effective method to generate 3D-conforming tetrahedral meshes from closed 2-manifold surfaces. Our approach is inspired by recent work on maximal Poisson-disk sampling (MPS), which can generate well-distributed point sets in arbitrary domains. We first perform MPS on the boundary of the input domain, we then sample the interior of the domain, and we finally extract the tetrahedral mesh from the samples by using 3D Delaunay or regular triangulation for uniform or adaptive sampling, respectively. We also propose an efficient optimization strategy to protect the domain boundaries and to remove slivers to improve the meshing quality. We present various experimental results to illustrate the efficiency and the robustness of our proposed approach. We demonstrate that the performance and quality (e.g., minimal dihedral angle) of our approach are superior to current state-of-the-art optimization-based approaches.

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New software developments for quality mesh generation and optimization from biomedical imaging data

Cited by 9 publications

References 56 publications

Direct volume manipulation for visualizing intraoperative liver resection process

Direct volume manipulation for visualizing intraoperative liver resection process

Image-based biomechanical models of the musculoskeletal system

Tetrahedral meshing via maximal Poisson-disk sampling

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