Evidence map›Paper›PMID 41495251›Full record

ArticleCardiovascular and interventional radiology2026

Automatic Bone Removal in CBCT Scans of the Body Trunk: Thorax, Abdomen, and Pelvis.

Hinrich Winther, Sabine Maschke, Lena Becker, Cornelia Dewald, Marcel Eicke, Tobias Jakobs, Roman Kloeckner, Axel Schmid, Frank Wacker, Bernhard Meyer

Abstract readMulticenter Study
In one paragraph

Article in Cardiovascular and interventional radiology, 2026. 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
–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

0 citing papers in PubMed.

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

10 authors.

Hinrich Winther *Department of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany. winther.hinrich@mh-hannover.de.ORCID http://orcid.org/0000-0001-6283-8042
Sabine Maschke *Department of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.
Lena BeckerDepartment of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.
Cornelia DewaldDepartment of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.
Marcel EickeDepartment of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.
Tobias JakobsClinic of Interventional Radiology, Hospital Barmherzige Brueder, 93049, Munich, Germany.
Roman KloecknerInstitute of Interventional Radiology, University Hospital Schleswig-Holstein Campus Lübeck, Lübeck, Germany.
Axel SchmidInstitute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany.
Frank WackerDepartment of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.
Bernhard MeyerDepartment of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30659, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate a fully automated bone removal software for cone beam computed tomography (CBCT) of the thorax, abdomen, and pelvis, enhancing vascular visualization by eliminating bone interference and improving diagnostic quality. MATERIAL AND

methods1035 CBCT scans from adults age 66.5 ± 11.9 18-87 years (mean ± std min-max) across nine centers were retrospectively analyzed, divided into training (n = 855, 515 abdomen, 229 pelvis, 111 thorax) and testing (n = 180, 60 for each region, 114 male, 53 female, 13 unknown). Manual bone segmentation was performed using ITK-SNAP. A modified 3D U-Net was trained and clinically evaluated through multireader analysis using ordinal scales from 1 (perfect) to 4 (not usable) bone subtraction (B-rating) and erosion of non-target structures (V-rating) in addition to a vessel assessment (VA-rating), categorizing the subtracted image as "better" (1), "same" (2), or "worse" (3). Quantitative metrics include Sørensen-Dice coefficient and intersection over union (IoU).

resultsThe software demonstrated high accuracy with a B-rating of 1.01 ± 0.07 and a V-rating of 1.02 ± 0.13, indicating minimal erosion of non-target structures. A VA-rating of 1.0 ± 0 suggests an improved vessel assessment and the depiction of contrast material deposition, enhancing the diagnostic quality of CBCT images. Quantitative analysis closely matched the manual expert delineation (Sørensen-Dice coefficient 0.95 ± 0.02, IoU of 0.9 ± 0.03).

conclusionThe software provides robust, fully automated bone removal in CBCT scans. This technology may enhance vascular system visualization without compromising non-target structures, potentially improving the accuracy and efficiency of interventional and diagnostic radiology procedures.

Indexed as

Bone and BonesCone-Beam Computed TomographyPelvisRadiography, AbdominalRadiography, ThoracicAdolescentAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSoftwareBone removalCBCTCone beam computed tomographyDeep learningImage guidanceImage segmentationVessel visualization

Identifiers

PMID41495251
PMCPMC12963259

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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.