Evidence map›Paper›PMID 41676239›Full record

ArticleHealthcare technology letters

Hidden Tumour Visualization in Augmented Monocular Liver Laparoscopy.

Kirana Hanifati, Mohammad Alkhatib, Erol Ozgur, Emmanuel Buc, Bertrand Le Roy, Hestiasari Rante, Youcef Mezouar, Adrien Bartoli

Abstract read
In one paragraph

Article in Healthcare technology letters. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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.

Kirana HanifatiClermont Auvergne INP Institut Pascal Clermont-Ferrand France.ORCID https://orcid.org/0009-0008-5874-2003
Mohammad AlkhatibClermont Auvergne INP Institut Pascal Clermont-Ferrand France.
Erol OzgurClermont Auvergne INP Institut Pascal Clermont-Ferrand France.
Emmanuel BucUniversity Hospital Clermont-Ferrand France.
Bertrand Le RoyUniversity Hospital Saint-Etienne France.
Hestiasari RantePoliteknik Elektronika Negeri Surabaya Surabaya Indonesia.
Youcef MezouarClermont Auvergne INP Institut Pascal Clermont-Ferrand France.
Adrien BartoliUniversity Hospital Clermont-Ferrand France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We address the hidden tumour visualization problem in augmented monocular liver laparoscopy. Conveying a hidden tumour's depth correctly to the surgeon in augmented monocular laparoscopy is extremely difficult and still forms an unsolved problem. The depth conveyance can be split into two subsequent problems. First, designing a visualization that convinces the user to see the tumour inside the organ. Second, enhancing this visualization so that it also provides metric depth perception. The most promising visualization methods rely on a preoperative CT organ model with the tumour to be registered to an intraoperative laparoscopic image. Such a registration allows the organ's intraoperative shape mesh to be overlaid on top of the augmented tumour. The overlaid organ mesh guarantees a partial occlusion on the augmented tumour. This provides a powerful depth cue for the surgeon's perception. However, this type of registration, especially in liver laparoscopy, is usually not real-time and sometimes not possible. This is because of the liver deformation and lack of matchable features between the multimodal images. Subsequently, the tumour augmentation cannot be carried out continuously to guide the surgeon. We propose a novel visualization method to address these limitations. The proposed method replaces the deformable preoperative to intraoperative liver registration with a rigid tumour registration via laparoscopic ultrasound imaging. The proposed method handles surgical tool occlusions, runs faster, and outperforms the state of the art in terms of depth perception, as shown in the user study.

Indexed as

augmented realitydata visualisationmedical image processing

Identifiers

PMID41676239
PMCPMC12889570

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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.