Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery.

Augmented reality Pedicle screw RGB-D Registration

Journal

Medical image analysis
ISSN: 1361-8423
Titre abrégé: Med Image Anal
Pays: Netherlands
ID NLM: 9713490

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 02 03 2023
revised: 29 10 2023
accepted: 09 11 2023
pubmed: 23 11 2023
medline: 23 11 2023
entrez: 22 11 2023
Statut: ppublish

Résumé

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon's perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance.

Identifiants

pubmed: 37992494
pii: S1361-8415(23)00287-6
doi: 10.1016/j.media.2023.103027
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103027

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mazda Farshad reports a relationship with Incremed AG that includes: board membership and equity or stocks.

Auteurs

Florentin Liebmann (F)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland. Electronic address: florentin.liebmann@balgrist.ch.

Marco von Atzigen (M)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland.

Dominik Stütz (D)

Computer Vision and Geometry Group, ETH Zurich, Zurich, Switzerland.

Julian Wolf (J)

Product Development Group, ETH Zurich, Zurich, Switzerland.

Lukas Zingg (L)

Department of Orthopedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Daniel Suter (D)

Department of Orthopedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Nicola A Cavalcanti (NA)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Department of Orthopedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Laura Leoty (L)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Hooman Esfandiari (H)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Jess G Snedeker (JG)

Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland.

Martin R Oswald (MR)

Computer Vision and Geometry Group, ETH Zurich, Zurich, Switzerland; Computer Vision Lab, University of Amsterdam, Amsterdam, Netherlands.

Marc Pollefeys (M)

Computer Vision and Geometry Group, ETH Zurich, Zurich, Switzerland; Microsoft Mixed Reality and AI Zurich Lab, Zurich, Switzerland.

Mazda Farshad (M)

Department of Orthopedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Philipp Fürnstahl (P)

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.

Classifications MeSH