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Related Experiment Video

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A monocular thoracoscopic 3D scene reconstruction framework based on NeRF.

Juntao Han1, Ziming Zhang1, Wenjun Tan2,3

  • 1School of Computer Science and Engineering, Northeastern University, No. 169, Baoyuan Street, Shenyang, Liaoning, 110819, China.

Medical & Biological Engineering & Computing
|February 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel implicit 3D reconstruction method for thoracoscopic scenes, enhancing surgical navigation. The approach improves depth accuracy and point cloud quality for complex thoracic surgeries.

Keywords:
Absolute depth estimationMonocular 3D reconstructionNeRFThoracoscopy

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Accurate 3D reconstruction is vital for surgical navigation and treatment, but traditional methods struggle with thoracoscopic scenes due to limited features and lighting variations.
  • Challenges in feature point matching lead to unstable and unreliable 3D reconstruction in medical imaging.

Purpose of the Study:

  • To develop a new implicit 3D reconstruction method for monocular thoracoscopic scenes.
  • To enhance the accuracy and structural consistency of 3D reconstructions for improved surgical navigation.

Main Methods:

  • Combined a pre-trained metric depth estimation model with Neural Radiance Fields (NeRF).
  • Utilized dense Simultaneous Localization and Mapping (SLAM) for accurate camera pose computation.
  • Incorporated depth and normal constraints alongside color constraints within the NeRF framework.

Main Results:

  • Achieved high-quality scene reconstruction results with improved depth accuracy and structural consistency.
  • Experimental results on SCARED and clinical datasets demonstrated superior performance compared to existing methods.
  • Outperformed other methods in depth estimation accuracy and point cloud reconstruction quality.

Conclusions:

  • The proposed method provides more accurate 3D reconstruction of complex thoracic surgical scenes.
  • This advancement can significantly improve the accuracy and therapeutic efficacy of surgical navigation systems.