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Novel view synthesis using neural radiance fields for laparoscopic surgery navigation.

Nati Nawawithan^1,2, James Yu^1,3, Kelden Pruitt^1,2

¹Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.

Proceedings of Spie--The International Society for Optical Engineering

|October 22, 2025

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This study enhances laparoscopic surgery visualization by generating synthetic data, improving training for computer vision algorithms like SLAM. This advances autonomous surgical navigation systems.

Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Laparoscopic surgery offers minimally invasive benefits but presents limited visualization challenges for surgeons.
Current surgical navigation systems aim to enhance visualization, often integrating computer algorithms for improved efficiency.

Purpose of the Study:

To augment existing laparoscopic image datasets with novel views, depths, and camera poses.
To provide a foundation for training and evaluating learning-based computer vision algorithms for surgical navigation.

Main Methods:

Generated novel views and depth information from existing laparoscopic porcine tissue images using the NeRF-SLAM algorithm.
Transformed camera poses to create synthetic RGB and depth images.
Utilized an optical tracking system for initial camera pose capture.

Keywords:

Laparoscopic surgery image-guided intervention neural radiance fields

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Main Results:

The generated synthetic RGB images achieved high Peak Signal-to-Noise Ratio (PSNR) between 29.17-29.79 dB.
Structural Similarity Index Measure (SSIM) values ranged from 0.6061-0.6174, indicating good image fidelity.
Learned Perceptual Image Patch Similarity (LPIPS) values were between 0.568-0.592, demonstrating perceptual realism.

Conclusions:

The NeRF-SLAM algorithm successfully reconstructed tissue surfaces and generated realistic synthetic laparoscopic data.
The synthetic dataset is valuable for training and evaluating computer vision algorithms, particularly for simultaneous localization and mapping (SLAM) and visual odometry.
This work supports the development of learning-based autonomous surgical navigation systems by providing essential training data.