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Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

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Efficient EndoNeRF reconstruction and its application for data-driven surgical simulation.

Yuehao Wang¹, Bingchen Gong¹, Yonghao Long¹

¹Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

International Journal of Computer Assisted Radiology and Surgery

|April 24, 2024

Summary

This summary is machine-generated.

This study introduces a novel framework using neural radiance fields (NeRF) for realistic 3D surgical scene reconstruction. This approach enhances surgical training and planning by creating high-quality soft tissue models for simulations.

Keywords:

3D reconstruction NeRF Robotic surgery Surgery simulation

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Area of Science:

Medical Simulation
Computer Graphics
Surgical Technology

Background:

Realistic surgical scene modeling is crucial for planning and training.
Existing methods struggle with accurate soft tissue shape and texture reconstruction.
High-quality deformable soft tissue models are scarce for surgical simulations.

Purpose of the Study:

To present a data-driven framework for high-quality surgical reconstruction using neural radiance fields (NeRF).
To enable realistic surgical simulations through accurate soft tissue modeling.
To address the scarcity of high-fidelity soft tissue models in healthcare.

Main Methods:

Developed a fast NeRF-based 3D reconstruction approach for surgical scenes.
Implemented a closed mesh extraction algorithm for automated simulation environment creation.
Outperformed traditional methods in capturing deformations and fine-grained details.

Main Results:

Validated superior performance and efficiency in surgical scene 3D reconstruction.
Successfully utilized reconstructed soft tissues for Finite Element Method (FEM) and Material Point Method (MPM) simulations.
Demonstrated practical application in data-driven surgical simulations.

Conclusions:

Proposed a novel NeRF-based reconstruction framework optimized for simulation.
Facilitated efficient creation of high-quality 3D surgical soft tissue models.
Showcased potential to benefit clinical tasks like surgical education through simulations.