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Related Experiment Video

Updated: Jul 2, 2025

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Neural radiance fields-based multi-view endoscopic scene reconstruction for surgical simulation.

Zhibao Qin1, Kai Qian2, Shaojun Liang1

  • 1Yunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, 650500, China.

International Journal of Computer Assisted Radiology and Surgery
|February 27, 2024
PubMed
Summary

We developed Endoscope-NeRF to create realistic 3D surgical scenes from endoscopic images. This method significantly improves virtual surgery training by generating high-fidelity novel views, outperforming existing techniques.

Keywords:
Endoscopic sceneNeural radiance fieldsNovel view synthesisSurgical simulationVolume rendering

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Virtual Reality

Background:

  • Current 3D models from CT scans lack realism for virtual surgery training.
  • Realistic endoscopic scene reconstruction is vital for surgical education and reducing misunderstandings.

Purpose of the Study:

  • To reconstruct realistic endoscopic scenes using multi-view images.
  • To improve the fidelity of virtual surgical environments.

Main Methods:

  • Proposed Endoscope-NeRF network for implicit radiance fields reconstruction.
  • Utilized multiple MLPs and a ray transformer network for scene representation.
  • Employed volume rendering to synthesize novel views, considering light source effects.

Main Results:

  • Validated on pig lung, liver, kidney, and heart datasets.
  • Synthesized novel views outperformed NeRF and IBRNet in PSNR, SSIM, and LPIPS metrics.
  • Demonstrated superior realism and accuracy in generated endoscopic scenes.

Conclusions:

  • Endoscope-NeRF effectively learns generalizable radiance field functions.
  • Fine-tuning enables optimization for specific endoscopic scenes.
  • Provides realistic, high-resolution images for enhanced surgical simulation.