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Incremental shape integration with inter-frame shape consistency using neural SDF for a 3D endoscopic system.

Ryo Furukawa1, Hiroshi Kawasaki2, Ryusuke Sagawa3

  • 1Department of Informatics/Graduate School of System Engineering Kindai University Higashihiroshima Japan.

Healthcare Technology Letters
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an incremental optimization technique for 3D endoscopic measurements using neural signed distance fields (NeuralSDF). The method effectively reconstructs 3D scenes and camera poses from endoscopic videos, enhancing active-stereo systems.

Keywords:
endoscopesimage processingmedical image processing

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Robotics

Background:

  • Accurate 3D measurement is crucial for endoscopic procedures.
  • Active-stereo systems with pattern projectors offer a promising solution.
  • Enlarging the reconstructed area requires multi-frame integration.

Purpose of the Study:

  • To develop an incremental optimization technique for 3D endoscopic measurements.
  • To simultaneously optimize 3D scene shape and camera/projector poses.
  • To handle continuously varying relative positions in endoscopic videos.

Main Methods:

  • Utilized differential volume rendering with neural signed distance field (NeuralSDF) representation.
  • Implemented an incremental optimization strategy for gradually increasing optimized frames.
  • Processed temporarily sequential images (endoscopic videos) as input data.

Main Results:

  • Successfully performed 3D reconstruction using both synthetic and real endoscopic images.
  • Demonstrated the effectiveness of the proposed incremental optimization technique.
  • Showcased simultaneous optimization of 3D scene and camera/projector poses.

Conclusions:

  • The proposed method effectively addresses the demand for 3D measurement in endoscopic systems.
  • The integration of NeuralSDF and differential volume rendering enables robust 3D reconstruction.
  • The incremental optimization strategy enhances the scalability and accuracy for dynamic endoscopic environments.