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

• URL: http://arxiv.org/abs/2505.18381v1
• Date: Fri, 23 May 2025 21:15:00 GMT
• Title: Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery
• Authors: Yike Zhang, Eduardo Davalos Anaya, Jack H. Noble,
• Abstract summary: Our framework seamlessly integrates with monocular surgical microscopes, making it highly practical for clinical use without additional hardware dependencies and requirements.<n>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.
• Score: 4.250558597144547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Surgical Neural Radiance Fields from One Image [1.7675961199872583]
  We leverage preoperative MRI data to define the set of camera viewpoints and images needed for robust and unobstructed training.<n>The appearance of the surgical image is transferred to the pre-constructed training set through neural style transfer.<n>This process enables the creation of a dataset for instant and fast single-image NeRF training.
  arXiv  Detail & Related papers  (2025-07-01T17:19:25Z)
• Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection [50.388465935739376]
  Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate.<n>Existing registration methods rely heavily on anatomical landmark-based, which encounter two major limitations.<n>We propose a landmark-free preoperative-to-intraoperative registration framework utilizing effective self-supervised learning.
  arXiv  Detail & Related papers  (2025-04-21T14:55:57Z)
• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery [3.8909273404657556]
  We develop a method that permits direct 2D-to-3D registration of the view microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively.
  arXiv  Detail & Related papers  (2024-03-12T00:26:08Z)
• An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion [33.12510773034339]
  Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image. Most of the algorithms make use of anatomical landmarks to guide registration. These landmarks include the liver's inferior ridge, the falciform ligament, and the occluding contours. We present the Preoperative-to-Intraoperative Laparoscopic Fusion Challenge (P2ILF), which investigates the possibilities of detecting these landmarks automatically and using them in registration.
  arXiv  Detail & Related papers  (2024-01-28T20:30:14Z)
• Learning Expected Appearances for Intraoperative Registration during Neurosurgery [39.256282185354465]
  We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations.
  arXiv  Detail & Related papers  (2023-10-03T01:50:48Z)
• Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries [29.201385352740555]
  We propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. A cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation.
  arXiv  Detail & Related papers  (2023-09-02T14:52:58Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [64.59698930334012]
  We present a multi-camera capture setup consisting of static and head-mounted cameras.<n>Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre.<n>Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.