Evidence map›Paper›PMID 38922721›Full record

ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2024

A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Manish Sahu, Yuliang Xiao, Jose L Porras, Ameen Amanian, Aseem Jain, Andrew Thamboo, Russell H Taylor, Francis X Creighton, Masaru Ishii

Abstract read
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In one paragraph

Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Integration of Trackerless Surface Reconstruction-Based Surgical Navigation With Exoscopic Trans-Mastoid Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
    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

9 authors.

Manish SahuLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0003-2158-823X
Yuliang XiaoLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland, USA.
Jose L PorrasDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Ameen AmanianDivision of Otolaryngology, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada.
Aseem JainLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland, USA.
Andrew ThambooDivision of Otolaryngology, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada.
Russell H TaylorLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland, USA.
Francis X CreightonDepartment of Otolaryngology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Masaru IshiiDepartment of Otolaryngology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Funding

Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base SurgeryK08DC019708 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI CREIGHTON, FRANCIS XAVIER · 2021 to 2025
$959k
NIDCD NIH HHS K08 DC019708
6 · The paper itself

Abstract

objectiveSegmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans. STUDY

designRetrospective cohort study.

settingAcademic institution.

methodsForty CT scans were used in this study including 16 scans in which the nasal septum (NS), inferior turbinate (IT), maxillary sinus (MS), and optic nerve (ON) were manually annotated using an open-source software. A label-efficient DL framework was used to train jointly on a few manually labeled scans and the remaining unlabeled scans. Quantitative analysis was then performed to obtain the number of annotated scans needed to achieve submillimeter average surface distances (ASDs).

resultsOur findings reveal that merely four labeled scans are necessary to achieve median submillimeter ASDs for large sinonasal structures-NS (0.96 mm), IT (0.74 mm), and MS (0.43 mm), whereas eight scans are required for smaller structures-ON (0.80 mm).

conclusionWe have evaluated a label-efficient pipeline for segmentation of sinonasal structures. Empirical results demonstrate that automated DL methods can achieve submillimeter accuracy using a small number of labeled CT scans. Our pipeline has the potential to improve pre-operative planning workflows, robotic- and image-guidance navigation systems, computer-assisted diagnosis, and the construction of statistical shape models to quantify population variations. LEVEL OF EVIDENCE: N/A.

Indexed as

Deep LearningSurgery, Computer-AssistedTomography, X-Ray ComputedHumansMaleMaxillary SinusNasal SeptumParanasal SinusesRetrospective StudiesTurbinatesautomated segmentation and registrationdeep learningimage‐guided surgerymedical image segmentationsemi‐supervised learningsinonasal computed tomography

Identifiers

PMID38922721
PMCPMC13600364

What OpenQuestion holds

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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.