Evidence map›Paper›PMID 34043144›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study.

Peter A J Pijpker, Tim S Oosterhuis, Max J H Witjes, Chris Faber, Peter M A van Ooijen, Jiří Kosinka, Jos M A Kuijlen, Rob J M Groen, Joep Kraeima

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2021. 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
0.1field-weighted citation impact, top 63% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

9 authors at 2 institutions in 1 country.

Peter A J Pijpker3D-Lab and Department of Neurosurgery, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713, GZ, Groningen, The Netherlands. p.a.j.pijpker@umcg.nl.ORCID http://orcid.org/0000-0001-9543-1097
Tim S Oosterhuis3D-Lab and Bernoulli Institute, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713, GZ, Groningen, The Netherlands.
Max J H Witjes3D-Lab and Department of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Chris FaberDepartment of Orthopedic Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Peter M A van OoijenDepartment of Radiation Oncology and Data Science Center in Health, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Jiří KosinkaBernoulli Institute, University of Groningen, Groningen, The Netherlands.
Jos M A KuijlenDepartment of Neurosurgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Rob J M GroenDepartment of Neurosurgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Joep Kraeima3D-Lab and Department of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
University Medical Center Groningen · NLUniversity of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this paper is to present and validate a new semi-automated 3D surface mesh segmentation approach that optimizes the laborious individual human vertebrae separation in the spinal virtual surgical planning workflow and make a direct accuracy and segmentation time comparison with current standard segmentation method.

methodsThe proposed semi-automatic method uses the 3D bone surface derived from CT image data for seed point-based 3D mesh partitioning. The accuracy of the proposed method was evaluated on a representative patient dataset. In addition, the influence of the number of used seed points was studied. The investigators analyzed whether there was a reduction in segmentation time when compared to manual segmentation. Surface-to-surface accuracy measurements were applied to assess the concordance with the manual segmentation.

resultsThe results demonstrated a statically significant reduction in segmentation time, while maintaining a high accuracy compared to the manual segmentation. A considerably smaller error was found when increasing the number of seed points. Anatomical regions that include articulating areas tend to show the highest errors, while the posterior laminar surface yielded an almost negligible error.

conclusionA novel seed point initiated surface based segmentation method for the laborious individual human vertebrae separation was presented. This proof-of-principle study demonstrated the accuracy of the proposed method on a clinical CT image dataset and its feasibility for spinal virtual surgical planning applications.

Indexed as

SpineTomography, X-Ray ComputedAlgorithmsHumansImaging, Three-Dimensional3D surface segmentationComputed tomographySeed point segmentationSegmentationSpineVertebraVirtual surgical planning (VSP)

Identifiers

PMID34043144
PMCPMC8354998
OpenAlexW3165343504

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.