Evidence map›Paper›PMID 32780240›Full record

ArticleSurgical endoscopy2020

Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study.

C Schneider, S Thompson, J Totz, Y Song, M Allam, M H Sodergren, A E Desjardins, D Barratt, S Ourselin, K Gurusamy and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Surgical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 pooled it
2.8field-weighted citation impact, top 8% of its field
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

34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 55 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Warm-started reinforcement learning for iterative 3D/2D liver registration.International journal of computer assisted radiology and surgery · 2026
    Article
  5. Laparoscopic liver point cloud registration via hyperbolic-topology interaction.International journal of computer assisted radiology and surgery · 2026
    Article
  6. Article
  7. Article
  8. Manual Registration in AR-Assisted Surgical Navigation: A Comparative Evaluation.International journal of computer assisted radiology and surgery · 2026
    Article
  9. Article
  10. Article
  11. Deep hashing for global registration of preoperative CT and video images for laparoscopic liver surgery.International journal of computer assisted radiology and surgery · 2025
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
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  20. Review
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

14 authors at 3 institutions in 1 country.

C SchneiderDivision of Surgery & Interventional Science, Royal Free Campus, University College London, Pond Street, London, NW3 2QG, UK. crispin.schneider.13@ucl.ac.uk.ORCID http://orcid.org/0000-0002-7253-3469
S ThompsonWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
J TotzWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
Y SongWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
M AllamDivision of Surgery & Interventional Science, Royal Free Campus, University College London, Pond Street, London, NW3 2QG, UK.
M H SodergrenCentre for Medical Image Computing (CMIC), University College London, London, UK.
A E DesjardinsWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
D BarrattWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
S OurselinWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
K GurusamyDivision of Surgery & Interventional Science, Royal Free Campus, University College London, Pond Street, London, NW3 2QG, UK.
D StoyanovWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
M J ClarksonWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
D J HawkesWellcome / EPSRC Centre for Surgical and Interventional Sciences (WEISS), University College London, London, UK.
B R DavidsonDivision of Surgery & Interventional Science, Royal Free Campus, University College London, Pond Street, London, NW3 2QG, UK.
Wellcome / EPSRC Centre for Interventional and Surgical Sciences · GBThe Royal Free Hospital · GBUniversity College London · GB

Funding

Department of Health UK HICF-T4-317Wellcome Trust 203145Z/16/Z
6 · The paper itself

Abstract

backgroundThe laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration.

methodsIntraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes.

resultsThe primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference - 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound.

conclusionThe technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

Indexed as

Augmented RealityAdultAgedAged, 80 and overFeasibility StudiesFemaleHumansLiverMaleMiddle AgedSurgery, Computer-AssistedAugmented realityComputer-assisted surgeryImage-guided surgeryLaparoscopic liver surgerySemi-automatic registrationStereoscopic surface reconstruction

Identifiers

PMID32780240
PMCPMC7524854
OpenAlexW3048453852

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.