Evidence map›Paper›PMID 34009539›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

Automated atlas-based segmentation for skull base surgical planning.

Neeraja Konuthula, Francisco A Perez, A Murat Maga, Waleed M Abuzeid, Kris Moe, Blake Hannaford, Randall A Bly

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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. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. High-Fidelity 3D Reconstruction for Accurate Anatomical Measurements in Endoscopic Sinus Surgery.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  3. A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2024
    Article
  4. Review
  5. Article
  6. 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

7 authors.

Neeraja KonuthulaDepartment of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-9691-9331
Francisco A PerezDepartment of Radiology, University of Washington, Seattle, WA, USA.
A Murat MagaDepartment of Craniofacial Medicine, University of Washington, Seattle, WA, USA.
Waleed M AbuzeidDepartment of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA.
Kris MoeDepartment of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA.
Blake HannafordDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Randall A BlyDepartment of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA. randbly@uw.edu.

Funding

Research Training in OtolaryngologyT32DC000018 · NIDCD · UNIVERSITY OF WASHINGTON · PI STONE, JENNIFER S. · 1989 to 2023
$7.8M
NIDCD NIH HHS T32 DC000018NIDCD NIH HHS T32 DC000018-34
6 · The paper itself

Abstract

purposeComputational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning.

methodsAdvanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets.

resultsThe mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections.

conclusionCurrently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259 ) with this paper will allow for general use of automated atlas-based segmentation of the skull base.

Indexed as

AlgorithmsSoftwareAdolescentAdultFemaleHumansImage Processing, Computer-AssistedMaleNeurosurgical ProceduresPreoperative CareSkull BaseYoung AdultAtlas-based registrationDeformable registrationSkull baseSurgical planning

Identifiers

PMID34009539
PMCPMC8317429

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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.