Evidence map›Paper›PMID 26657513›Full record

ArticlePloS one2015

Airway Segmentation and Centerline Extraction from Thoracic CT - Comparison of a New Method to State of the Art Commercialized Methods.

Pall Jens Reynisson, Marta Scali, Erik Smistad, Erlend Fagertun Hofstad, Håkon Olav Leira, Frank Lindseth, Toril Anita Nagelhus Hernes, Tore Amundsen, Hanne Sorger, Thomas Langø

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

20 citing papers in PubMed, 57 citations in OpenAlex.

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  11. SBRT of ventricular tachycardia using 4pi optimized trajectories.Journal of applied clinical medical physics · 2021
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  14. Computed tomography-based bronchial tree three-dimensional reconstruction and airway resistance evaluation in adolescent idiopathic scoliosis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2020
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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

10 authors at 5 institutions in 2 countries.

Pall Jens ReynissonDept. Circulation and medical imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Marta ScaliBio-Mechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, Netherlands.
Erik SmistadDept. Computer and Information Science, NTNU, Trondheim, Norway.
Erlend Fagertun HofstadDept. Medical Technology, SINTEF, Trondheim, Norway.
Håkon Olav LeiraDept. Circulation and medical imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Frank LindsethDept. Computer and Information Science, NTNU, Trondheim, Norway.
Toril Anita Nagelhus HernesDept. Circulation and medical imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Tore AmundsenDept. Circulation and medical imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Hanne SorgerDept. Circulation and medical imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Thomas LangøDept. Medical Technology, SINTEF, Trondheim, Norway.
Norwegian University of Science and Technology · NOSINTEF · NODelft University of Technology · NLNTNU Samfunnsforskning · NOSt Olav's University Hospital · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOur motivation is increased bronchoscopic diagnostic yield and optimized preparation, for navigated bronchoscopy. In navigated bronchoscopy, virtual 3D airway visualization is often used to guide a bronchoscopic tool to peripheral lesions, synchronized with the real time video bronchoscopy. Visualization during navigated bronchoscopy, the segmentation time and methods, differs. Time consumption and logistics are two essential aspects that need to be optimized when integrating such technologies in the interventional room. We compared three different approaches to obtain airway centerlines and surface.

methodCT lung dataset of 17 patients were processed in Mimics (Materialize, Leuven, Belgium), which provides a Basic module and a Pulmonology module (beta version) (MPM), OsiriX (Pixmeo, Geneva, Switzerland) and our Tube Segmentation Framework (TSF) method. Both MPM and TSF were evaluated with reference segmentation. Automatic and manual settings allowed us to segment the airways and obtain 3D models as well as the centrelines in all datasets. We compared the different procedures by user interactions such as number of clicks needed to process the data and quantitative measures concerning the quality of the segmentation and centrelines such as total length of the branches, number of branches, number of generations, and volume of the 3D model.

resultsThe TSF method was the most automatic, while the Mimics Pulmonology Module (MPM) and the Mimics Basic Module (MBM) resulted in the highest number of branches. MPM is the software which demands the least number of clicks to process the data. We found that the freely available OsiriX was less accurate compared to the other methods regarding segmentation results. However, the TSF method provided results fastest regarding number of clicks. The MPM was able to find the highest number of branches and generations. On the other hand, the TSF is fully automatic and it provides the user with both segmentation of the airways and the centerlines. Reference segmentation comparison averages and standard deviations for MPM and TSF correspond to literature.

conclusionThe TSF is able to segment the airways and extract the centerlines in one single step. The number of branches found is lower for the TSF method than in Mimics. OsiriX demands the highest number of clicks to process the data, the segmentation is often sparse and extracting the centerline requires the use of another software system. Two of the software systems performed satisfactory with respect to be used in preprocessing CT images for navigated bronchoscopy, i.e. the TSF method and the MPM. According to reference segmentation both TSF and MPM are comparable with other segmentation methods. The level of automaticity and the resulting high number of branches plus the fact that both centerline and the surface of the airways were extracted, are requirements we considered particularly important. The in house method has the advantage of being an integrated part of a navigation platform for bronchoscopy, whilst the other methods can be considered preprocessing tools to a navigation system.

Indexed as

AlgorithmsImage Processing, Computer-AssistedRadiography, ThoracicTomography, X-Ray ComputedHumansLungSoftwareUser-Computer Interface

Identifiers

PMID26657513
PMCPMC4676651
OpenAlexW2190602582

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.