Evidence map›Paper›PMID 37079247›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

Visualization in 2D/3D registration matters for assuring technology-assisted image-guided surgery.

Sue Min Cho, Robert B Grupp, Catalina Gomez, Iris Gupta, Mehran Armand, Greg Osgood, Russell H Taylor, Mathias Unberath

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Uncertainty Quantification in Image-based 2D/3D Registration and Its Relationship with Accuracy.International journal of computer assisted radiology and surgery · 2025
    Article
  3. Autonomous Robotic Systems in Otolaryngology-Head and Neck Surgery.Otolaryngologic clinics of North America · 2024
    Review
  4. Article
  5. Article
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

8 authors.

Sue Min ChoJohns Hopkins University, Baltimore, MD, USA. scho72@jhu.edu.ORCID http://orcid.org/0000-0002-6677-7360
Robert B GruppJohns Hopkins University, Baltimore, MD, USA.
Catalina GomezJohns Hopkins University, Baltimore, MD, USA.
Iris GuptaJohns Hopkins University, Baltimore, MD, USA.
Mehran ArmandJohns Hopkins University, Baltimore, MD, USA.
Greg OsgoodJohns Hopkins School of Medicine, Baltimore, MD, USA.
Russell H TaylorJohns Hopkins University, Baltimore, MD, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, USA.

Funding

Image-Guided Workstation and Tools for the Treatment of Bone DefectsR01EB016703 · NIBIB · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN · 2013 to 2022
$3.4M
Robotic System for Spinal Decompression and Interbody FusionR01AR080315 · NIAMS · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN, JAIN, AMIT · 2022 to 2025
$1.7M
Robot-Assisted Femoroplasty with Intraoperative Biomechanical FeedbackR01EB023939 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI ARMAND, MEHRAN · 2017 to 2020
$1.6M
NIAMS NIH HHS R01 AR080315NIBIB NIH HHS R01 EB016703NIBIB NIH HHS R01 EB023939
6 · The paper itself

Abstract

purposeImage-guided navigation and surgical robotics are the next frontiers of minimally invasive surgery. Assuring safety in high-stakes clinical environments is critical for their deployment. 2D/3D registration is an essential, enabling algorithm for most of these systems, as it provides spatial alignment of preoperative data with intraoperative images. While these algorithms have been studied widely, there is a need for verification methods to enable human stakeholders to assess and either approve or reject registration results to ensure safe operation.

methodsTo address the verification problem from the perspective of human perception, we develop novel visualization paradigms and use a sampling method based on approximate posterior distribution to simulate registration offsets. We then conduct a user study with 22 participants to investigate how different visualization paradigms (Neutral, Attention-Guiding, Correspondence-Suggesting) affect human performance in evaluating the simulated 2D/3D registration results using 12 pelvic fluoroscopy images.

resultsAll three visualization paradigms allow users to perform better than random guessing to differentiate between offsets of varying magnitude. The novel paradigms show better performance than the neutral paradigm when using an absolute threshold to differentiate acceptable and unacceptable registrations (highest accuracy: Correspondence-Suggesting (65.1%), highest F1 score: Attention-Guiding (65.7%)), as well as when using a paradigm-specific threshold for the same discrimination (highest accuracy: Attention-Guiding (70.4%), highest F1 score: Corresponding-Suggesting (65.0%)).

conclusionThis study demonstrates that visualization paradigms do affect the human-based assessment of 2D/3D registration errors. However, further exploration is needed to understand this effect better and develop more effective methods to assure accuracy. This research serves as a crucial step toward enhanced surgical autonomy and safety assurance in technology-assisted image-guided surgery.

Indexed as

Imaging, Three-DimensionalSurgery, Computer-AssistedAlgorithmsFluoroscopyHumansPelvisTechnology2D/3D registrationAssured autonomyImage-guided surgeryPerceptionVisualization

Identifiers

PMID37079247
PMCPMC10986429

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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.