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

arXiv: https://arxiv.org/abs/2505.18381 · HTML: https://mlsanity.com/paper/2505.18381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 13 of 13)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [GridMask Data Augmentation](https://mlsanity.com/paper/2001.04086/) (2020)
- [Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2](https://mlsanity.com/paper/2408.01648/) (2024)
- [SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)](https://mlsanity.com/paper/2308.11774/) (2023)
- [Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery](https://mlsanity.com/paper/2403.07219/) (2024)
- [Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models](https://mlsanity.com/paper/2410.07434/) (2024)
- [Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery](https://mlsanity.com/paper/2407.15787/) (2024)
- [Post-mastoidectomy Surface Multi-View Synthesis from a Single Microscopy Image](https://mlsanity.com/paper/2409.03190/) (2024)
- [Learning Expected Appearances for Intraoperative Registration during Neurosurgery](https://mlsanity.com/paper/2310.01735/) (2023)
- [SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant Surgical Scene Completion](https://mlsanity.com/paper/2502.05710/) (2025)
- [Cochlear Implantation of Slim Pre-curved Arrays using Automatic Pre-operative Insertion Plans](https://mlsanity.com/paper/2410.18366/) (2024)

## Abstract (arXiv)

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.