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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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Intraoperative Registration by Cross-Modal Inverse Neural Rendering.

Maximilian Fehrentz^1,2, Mohammad Farid Azampour², Reuben Dorent¹

¹Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|December 22, 2025

Summary

This summary is machine-generated.

This study introduces a new method for 3D/2D intraoperative registration in neurosurgery using neural rendering. It improves accuracy and meets clinical standards for surgical navigation.

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Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Published on: January 11, 2020

Area of Science:

Medical Imaging
Computer Vision
Neurosurgery

Background:

Accurate 3D/2D intraoperative registration is crucial for neurosurgical navigation.
Existing methods face challenges in real-time accuracy and adaptability.

Purpose of the Study:

To develop a novel cross-modal inverse neural rendering approach for 3D/2D intraoperative registration in neurosurgery.
To enhance the precision and reliability of surgical navigation systems.

Main Methods:

Separating implicit neural representation into anatomical structure and appearance components.
Utilizing a multi-style hypernetwork to control appearance within a Neural Radiance Field.
Employing a differentiable rendering engine for surgical camera pose estimation.

Main Results:

The proposed method demonstrates superior performance compared to state-of-the-art registration techniques.
The approach meets current clinical standards for registration accuracy in neurosurgery.
Validation was performed on retrospective clinical case data.

Conclusions:

The novel neural rendering approach offers a significant advancement in 3D/2D intraoperative registration for neurosurgery.
This method has the potential to improve surgical outcomes through enhanced navigation.
The developed technique provides a robust and accurate solution for real-time surgical guidance.