Evidence map›Paper›PMID 38013837›Full record

ArticleComputer methods in biomechanics and biomedical engineering. Imaging & visualization2022

Deep homography estimation in dynamic surgical scenes for laparoscopic camera motion extraction.

Martin Huber, Sébastien Ourselin, Christos Bergeles, Tom Vercauteren

Abstract read
In one paragraph

Article in Computer methods in biomechanics and biomedical engineering. Imaging & visualization, 2022. 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. Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025
    Review
  2. Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023
    Article
  3. Synthetic white balancing for intra-operative hyperspectral imaging.Journal of medical imaging (Bellingham, Wash.) · 2023
    Article
  4. Article
  5. Homography-based Visual Servoing with Remote Center of Motion for Semi-autonomous Robotic Endoscope Manipulation.... International Symposium on Medical Robotics. International Symposium on Medical Robotics · 2021
    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

4 authors.

Martin HuberSchool of Biomedical Engineering & Image Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.ORCID https://orcid.org/0000-0003-4603-6773
Sébastien OurselinSchool of Biomedical Engineering & Image Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.ORCID https://orcid.org/0000-0002-5694-5340
Christos BergelesSchool of Biomedical Engineering & Image Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.ORCID https://orcid.org/0000-0002-9152-3194
Tom VercauterenSchool of Biomedical Engineering & Image Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.ORCID https://orcid.org/0000-0003-1794-0456

Funding

Wellcome Trust 101957Wellcome Trust 203148
6 · The paper itself

Abstract

Current laparoscopic camera motion automation relies on rule-based approaches or only focuses on surgical tools. Imitation Learning (IL) methods could alleviate these shortcomings, but have so far been applied to oversimplified setups. Instead of extracting actions from oversimplified setups, in this work we introduce a method that allows to extract a laparoscope holder's actions from videos of laparoscopic interventions. We synthetically add camera motion to a newly acquired dataset of camera motion free da Vinci surgery image sequences through a novel

Indexed as

Deep learninghomography estimationimage processing and analysislaparoscopic surgeryvirtual realityvisual data mining and knowledge discovery

Identifiers

PMID38013837
PMCPMC10478259

What OpenQuestion holds

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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.