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Mesh Analysis01:20

Mesh Analysis

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...
Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law (KVL)...

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Related Experiment Video

Updated: Jun 17, 2026

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
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GeoTranMesh: a geometry-guided multi-branch mesh transformer for 3d liver segmentation.

Jiaming Feng1, Xukun Zhang2, Shahid Farid3

  • 1AI in Medicine and Surgery Group, School of Computer Science, University of Leeds, LS2 9JT, Leeds, UK.

International Journal of Computer Assisted Radiology and Surgery
|April 6, 2026
PubMed
Summary

GeoTranMesh accurately segments complex liver surface anatomy using a novel geometry-guided mesh Transformer. This technology enhances precision for surgical navigation and anatomical studies.

Keywords:
3D landmark segmentationAR-guided surgeryLiver surfaceMesh transformer

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Area of Science:

  • Medical Imaging
  • Computational Geometry
  • Surgical Navigation

Background:

  • Accurate segmentation of liver surface anatomical landmarks like the falciform ligament and hepatic ridge is critical for intraoperative navigation.
  • These landmarks present complex geometry and significant morphological variability, posing challenges for precise identification on 3D liver meshes.

Purpose of the Study:

  • To introduce GeoTranMesh, a geometry-guided multi-branch mesh Transformer designed for high-precision segmentation of 3D liver meshes.
  • To address the need for accurate segmentation of complex anatomical structures on the liver surface for improved surgical guidance.

Main Methods:

  • Employs a hierarchical encoding-decoding structure and global geometric modeling.
  • Utilizes a hybrid attention mechanism to fuse local geometric features with cross-branch contextual information.
  • Incorporates a directional multi-branch fusion module and geometry-guided multi-task supervision (boundary, distance, normal regression) to enhance geometric consistency and morphological feature learning.

Main Results:

  • GeoTranMesh achieved superior segmentation accuracy on a liver mesh dataset.
  • Reported Dice scores of 30.9% for the falciform ligament and 66.4% for the liver ridge, with an overall Dice score of 59.5%.
  • Demonstrated excellent geometric consistency with a Chamfer distance of 4.4 mm.

Conclusions:

  • GeoTranMesh effectively enhances geometric consistency and morphological feature learning through its innovative attention and fusion modules.
  • The model achieves precise segmentation of complex anatomical landmarks on the liver.
  • Presents significant potential for applications in clinical settings and augmented reality (AR)-guided liver surgery.