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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

1.4K
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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MCVoxel2MeshNet: Voxel-To-Mesh Generation for Mandibular Canal Segmentation in CBCT.

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    This summary is machine-generated.

    MCVoxel2MeshNet accurately segments the mandibular canal (MC) in CBCT images using neural implicit representations. This method improves 3D mesh quality and reduces segmentation errors for better surgical planning.

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    Area of Science:

    • Medical Imaging
    • Computer-Aided Surgery
    • Computational Anatomy

    Background:

    • Accurate mandibular canal (MC) segmentation is vital for maxillofacial surgery planning.
    • Manual segmentation is time-consuming and variable.
    • Current CNN models struggle with spatial discontinuities and surface quality.

    Purpose of the Study:

    • To develop an automated MC segmentation method for cone-beam computed tomography (CBCT) images.
    • To improve segmentation accuracy and 3D mesh quality compared to existing methods.
    • To enhance computer-assisted maxillofacial surgical planning.

    Main Methods:

    • Proposed MCVoxel2MeshNet, a neural implicit representation (NIR)-based voxel-to-mesh network.
    • Modeled MC surface as a continuous function for seamless segmentation.
    • Introduced a modality-aware loss function with multi-level supervision (mask, grid, point, mesh).

    Main Results:

    • MCVoxel2MeshNet achieved a Dice Score (DS) of 0.85 ± 0.06% and Chamfer Distance (CD) of 1.97 ± 1.85 mm.
    • Outperformed existing segmentation networks in quantitative and qualitative evaluations.
    • Produced continuous, noise-free MC structures, eliminating false positives and artifacts.

    Conclusions:

    • MCVoxel2MeshNet offers accurate and robust MC segmentation in CBCT images.
    • The NIR-based approach enables high-fidelity 3D mesh generation.
    • This method has the potential to significantly enhance computer-assisted maxillofacial surgical planning.