Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery

TL;DR

This paper introduces a marker-free, real-time intraoperative registration method for cochlear implant surgery using monocular images and neural networks, eliminating the need for external hardware or fiducial markers.

Contribution

It presents a novel neural network-based framework that directly maps preoperative CT scans to intraoperative images without external tracking, suitable for clinical use.

Findings

Achieves clinically relevant accuracy with less than 10° angular error in most cases

Operates in real-time without external hardware or fiducial markers

Validated on nine clinical cases with promising results

Abstract

This paper presents a novel method for monocular patient-to-image intraoperative registration, specifically designed to operate without any external hardware tracking equipment or fiducial point markers. Leveraging a synthetic microscopy surgical scene dataset with a wide range of transformations, our approach directly maps preoperative CT scans to 2D intraoperative surgical frames through a lightweight neural network for real-time cochlear implant surgery guidance via a zero-shot learning approach. Unlike traditional methods, our framework seamlessly integrates with monocular surgical microscopes, making it highly practical for clinical use without additional hardware dependencies and requirements. Our method estimates camera poses, which include a rotation matrix and a translation vector, by learning from the synthetic dataset, enabling accurate and efficient intraoperative registration. The proposed framework was evaluated on nine clinical cases using a patient-specific and cross-patient validation strategy. Our results suggest that our approach achieves clinically relevant accuracy in predicting 6D camera poses for registering 3D preoperative CT scans to 2D surgical scenes with an angular error within 10 degrees in most cases, while also addressing limitations of traditional methods, such as reliance on external tracking systems or fiducial markers.