Evidence map›Paper›PMID 42101786›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Global region reidentification for camera relocalization in video-based surgical navigation.

Roger D Soberanis-Mukul, Ryan Chou, Chin Hang Ryan Chan, Jan Emily Mangulabnan, Lalithkumar Seenivasan, Simon Bonaventura Ertlmaier, S Swaroop Vedula, Russell H Taylor, Masaru Ishii, Gregory Hager and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Roger D Soberanis-MukulJohns Hopkins University, Baltimore, MD, 21211, USA. rsobera1@jhu.edu.ORCID http://orcid.org/0000-0003-0712-0534
Ryan ChouJohns Hopkins University, Baltimore, MD, 21211, USA.
Chin Hang Ryan ChanJohns Hopkins University, Baltimore, MD, 21211, USA.
Jan Emily MangulabnanJohns Hopkins University, Baltimore, MD, 21211, USA.
Lalithkumar SeenivasanJohns Hopkins University, Baltimore, MD, 21211, USA.
Simon Bonaventura ErtlmaierJohns Hopkins University, Baltimore, MD, 21211, USA.
S Swaroop VedulaJohns Hopkins University, Baltimore, MD, 21211, USA.
Russell H TaylorJohns Hopkins University, Baltimore, MD, 21211, USA.
Masaru IshiiJohns Hopkins Medical Institutions, Baltimore, MD, 21287, USA.
Gregory HagerJohns Hopkins University, Baltimore, MD, 21211, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, 21211, USA. unberath@jhu.edu.

Funding

Improved Surgical Navigation Using Video-CT RegistrationR01EB030511 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI HAGER, GREGORY DONALD, ISHII, MASARU · 2021 to 2024
$2.4M
NIH HHS R01EB030511
6 · The paper itself

Abstract

purposeVision-based navigation systems rely on the registered camera poses in the CT space to guide surgeons. However, while it is possible to provide an approximate initialization, this registration becomes outdated as the endoscopic camera leaves and reenters the anatomy. Endoscopic camera relocalization is the process of determining the position of an endoscope relative to an anatomical reference after reinsertion. However, accurately reidentifying the global surgical scene and estimating camera pose have proven challenging due to the varying appearance of endoscopic sequences.

methodsWe present a training-free approach to accurately reidentify the region of interest (ROI) and estimate the camera position of a query image after reinsertion. This method utilizes previously observed images with known poses and a CT scan. By combining advanced foundation models with classical techniques, we globally reidentify a prior image of the ROI, which is then used for image-based feature matching and pose recovery via the Perspective-n-Point algorithm.

resultsWe conducted experiments on eight sequences from three cadaver studies. Our results show that our method accurately reidentifies when the endoscope reaches the ROI and identifies suitable image pairs for PnP-based pose estimation. It achieves an average translation error of 1.74 mm and a rotational error of 0.09 radians, making it suitable for reinitialization in image-based navigation without human intervention.

conclusionOur work presents a training-free approach for detecting when the endoscope reenters the ROI and estimating the camera's pose after reinsertions. The approach demonstrates promising results contributing toward enabling pose reinitialization for vision-based surgical applications.

Indexed as

Surgery, Computer-AssistedTomography, X-Ray ComputedVideo-Assisted SurgeryAlgorithmsCadaverHumansCamera relocalizationPose estimationSurgical navigation

Identifiers

PMID42101786

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.