ArticleAcademic radiology2020
Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software.
Article in Academic radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.Yearbook of medical informatics · 2022Pooled it
- Radiomic profiling of chest CT in a cohort of sarcoidosis cases.Scientific reports · 2026Article
- The textures of sarcoidosis: quantifying lung disease through variograms.Physics in medicine and biology · 2025Article
- The textures of sarcoidosis: quantifying lung disease through variograms.medRxiv : the preprint server for health sciences · 2024Article
- Performance of virtual non-contrast images generated on clinical photon-counting detector CT for emphysema quantification: proof of concept.The British journal of radiology · 2022Article
- Cluster activation mapping with application to computed tomography scans of the lung.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
RATIONALE AND
objectivesA standard lung template could improve population-level analyses for computed tomography (CT) scans of the lung. We develop a fully automated preprocessing pipeline for image analysis of the lungs using updated methodologies and R software that results in the creation of a standard lung template. We apply this pipeline to CT scans from a sarcoidosis population, exploring the influence of registration on radiomic analyses. MATERIALS AND
methodsUsing 65 high-resolution CT scans from healthy adults, we create a standard lung template by segmenting the left and right lungs, nonlinearly registering lung masks to an initial template mask, and using an unbiased, iterative procedure to converge to a standard lung shape (Dice similarity coefficient ≥0.99). We compare three-dimensional radiomic features between control and sarcoidosis patients, before and after registration to a study-specific lung template.
resultsThe final lung template had a right lung volume of 2967 cm
conclusionThe final lung template and software used for its creation are publicly available via the lungct R package to facilitate its use in practice. This study advances lung imaging by developing tools to improve population-level analyses for various lung diseases.
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Registered trials
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.