Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Stretcher: a learning-based framework for deformation-robust keypoint descriptors.

International journal of computer assisted radiology and surgery·2026
Same author

A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration.

IEEE transactions on medical imaging·2026
Same author

Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

Intraoperative Registration by Cross-Modal Inverse Neural Rendering.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Two Projections Suffice for Cerebral Vascular Reconstruction.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025

Related Experiment Video

Updated: Sep 18, 2025

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis
10:57

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis

Published on: February 1, 2022

3.2K

Surgical neural radiance fields from one image.

Alberto Neri1,2,3, Maximilan Fehrentz4,5, Veronica Penza6

  • 1Biomedical Robotics Lab, Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy. alberto.neri@iit.it.

International Journal of Computer Assisted Radiology and Surgery
|June 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for single-image Neural Radiance Fields (NeRF) training in surgery. It enables fast, accurate 3D reconstruction from limited intraoperative data, overcoming traditional multi-view limitations.

More Related Videos

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.4K
Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
09:53

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Published on: July 5, 2021

3.7K

Related Experiment Videos

Last Updated: Sep 18, 2025

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis
10:57

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis

Published on: February 1, 2022

3.2K
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.4K
Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
09:53

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Published on: July 5, 2021

3.7K

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Surgical Technology

Background:

  • Neural Radiance Fields (NeRF) excel at 3D reconstruction but require extensive multi-view data, limiting their use in intraoperative surgical settings.
  • Limited intraoperative data availability poses a significant challenge for traditional NeRF applications in surgery.

Purpose of the Study:

  • To develop an efficient NeRF training method for surgical scenarios using a single intraoperative image and preoperative data.
  • To overcome the data limitations of conventional NeRF by enabling training with minimal surgical views.

Main Methods:

  • Leveraging preoperative MRI data to establish camera viewpoints and images for NeRF training.
  • Employing neural style transfer (WTC² and STROTSS) to adapt intraoperative image appearance to the pre-constructed dataset, preventing over-stylization.
  • Creating a dataset for rapid, single-image NeRF training.

Main Results:

  • The method was validated on four clinical neurosurgical cases.
  • Quantitative analysis showed high synthesis agreement and reconstruction fidelity compared to NeRF models trained on real surgical images.
  • High structural similarity metrics confirmed excellent reconstruction quality and texture preservation against ground truth.

Conclusions:

  • The proposed approach successfully demonstrates single-image NeRF training feasibility in surgical environments.
  • This method eliminates the need for large multi-view datasets, offering a faster and more adaptable solution for real-time 3D surgical reconstruction.
  • The technique provides accurate 3D reconstructions in real-time surgical scenarios.