ArticleIEEE transactions on bio-medical engineering2022
Pose Estimation and Non-Rigid Registration for Augmented Reality During Neurosurgery.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 24 citations in OpenAlex.
- A 3-D Cross-Modal Keypoint Descriptor for MR-US Matching and Registration.IEEE transactions on medical imaging · 2026Article
- Article
- A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions.Frontiers in neural circuits · 2026Article
- Intraoperative Registration by Cross-Modal Inverse Neural Rendering.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Augmented Reality in Neurosurgery.Advances in experimental medicine and biology · 2024Review
- Learning Expected Appearances for Intraoperative Registration during Neurosurgery.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- A Tool-free Neuronavigation Method based on Single-view Hand Tracking.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023Article
- Intra-operative applications of augmented reality in glioma surgery: a systematic review.Frontiers in surgery · 2023Review
- Visualization, navigation, augmentation. The ever-changing perspective of the neurosurgeon.Brain & spine · 2022Review
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
objectiveA craniotomy is the removal of a part of the skull to allow surgeons to have access to the brain and treat tumors. When accessing the brain, a tissue deformation occurs and can negatively influence the surgical procedure outcome. In this work, we present a novel Augmented Reality neurosurgical system to superimpose pre-operative 3D meshes derived from MRI onto a view of the brain surface acquired during surgery.
methodsOur method uses cortical vessels as main features to drive a rigid then non-rigid 3D/2D registration. We first use a feature extractor network to produce probability maps that are fed to a pose estimator network to infer the 6-DoF rigid pose. Then, to account for brain deformation, we add a non-rigid refinement step formulated as a Shape-from-Template problem using physics-based constraints that helps propagate the deformation to sub-cortical level and update tumor location.
resultsWe tested our method retrospectively on 6 clinical datasets and obtained low pose error, and showed using synthetic dataset that considerable brain shift compensation and low TRE can be achieved at cortical and sub-cortical levels.
conclusionThe results show that our solution achieved accuracy below the actual clinical errors demonstrating the feasibility of practical use of our system. SIGNIFICANCE: This work shows that we can provide coherent Augmented Reality visualization of 3D cortical vessels observed through the craniotomy using a single camera view and that cortical vessels provide strong features for performing both rigid and non-rigid registration.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.