A Two-Step Surface Reconstruction Method Using Signed Marching Cubes

Zhang, Ju; Zhong, Deyun; Wang, Liguan

doi:10.3390/app12041792

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Different combinations of the batch size, momentum, and number of heads are experimented with. The highest accuracy of 99.34% is obtained with a batch size of 64, a momentum of 0.9, and 8 heads in 45 s. The results of the ablation study indicate that the model achieves the highest accuracy with activation function: relu, a hidden unit of [32], 4 attention layers, a learning rate of 0.000001, a batch size of 64, a momentum of 0.9, and 8 heads.…”

Section: Ablation Studymentioning

confidence: 88%

“…After calculating the signed distance for each point of the point cloud, the process of 3D surface reconstruction, also known as isosurface, is carried out by the marching cube algorithm. The marching cube algorithm generates triangles of the signed distance point in a voxel grid to approximate the target isosurface [32]. Finally, the 3D point cloud obtained from the 2D image is converted into a solid mesh.…”

Section: Mesh Dataset Generation Using Point-e Networkmentioning

confidence: 99%

“…In the first experiment, three different activation functions, exponential linear unit (elu), rectified linear unit (relu), and hyperbolic tangent function (tanh), are used and the highest accuracy of 96.71% is attained with the relu activation function. Next, two types of hidden units [32] and [64] are experimented with and a test accuracy of 97.37% is achieved with the [32] hidden unit. In the third experiment, the model is tested with 2, 3, and 4 attention layers and the best accuracy, of 98.03%, is obtained with four layers.…”

Section: Ablation Studymentioning

confidence: 99%

See 2 more Smart Citations

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

J Digit Imaging. Inform. med.

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This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature’s significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.

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Section: Ablation Studymentioning

confidence: 88%

Section: Mesh Dataset Generation Using Point-e Networkmentioning

confidence: 99%

Section: Ablation Studymentioning

confidence: 99%

See 1 more Smart Citation

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

J Digit Imaging. Inform. med.

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rupMC: a ray-unit parallel marching cubes algorithm on CPU/GPU heterogeneous architectures

Yang,

Yun,

Guan

et al. 2024

International Journal of Digital Earth

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Finite element modelling of complex 3D image data with quantification and analysis

Chakkour

2024

Oxford Open Materials Science

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The purpose of this study was to examine how to model aggregated material microstructure and its meshing volumic generation that is provided by any data from 3D tomographic image data. The accurate reconstruction of 3D geometry structures from tomographic images is a powerful method in various application areas of materials science. The resulting mesh can be voxelized or conforming based on volumetric tetrahedral meshing. We investigate this creation depending on improving multiple materials marching cubes algorithm (M3C) with smoothing and remeshing algorithms. Then, a strategy for generating good-quality meshing and its robustness is presented, and this is performed with numerical tests. The novelty of this study is to generate a conforming mesh from complicated topology structures, particularly, when the interfaces of bi-materials are connected. This leads to a reduction in the node count in the generated mesh. The influence of some parameters involved in this algorithm is explored during different levels of meshing. In this work, the numerical homogenization approach from various spherical inclusions in the two-phase system using the algorithm M3C is considered to estimate the effective elastic properties. We created the framework with all the associated information, such as inputs in the format .inp files, to make it possible to run it over the Abaqus solver. Then, the Abaqus model based on the finite element method (FEM) was executed in this case for various material microstructures such as polycrystalline, composite, and fiber. We show the main workflow for providing desired results by visualizing the FEM analysis. We also demonstrate the capabilities of meshing methodology in the solver for these material models. The validation of the local mechanical environment from FEM with loading scenarios is achieved to predict displacements and deformations. Mechanical compression tests are performed to investigate the compressive behavior. Finally, stress-strain curves provided a comparison between simulations and experimental data for materials, and a good agreement is obtained.

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A Two-Step Surface Reconstruction Method Using Signed Marching Cubes

Cited by 5 publications

References 33 publications

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

rupMC: a ray-unit parallel marching cubes algorithm on CPU/GPU heterogeneous architectures

Finite element modelling of complex 3D image data with quantification and analysis

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