ArticleRespiratory research2020
Automatization and improvement of μCT analysis for murine lung disease models using a deep learning approach.
Article in Respiratory research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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
9 citing papers in PubMed.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Multidimensional lung imaging: integrated preclinical platforms enabling the identification of translational biomarkers for pulmonary research.Respiratory research · 2026Review
- A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.Respiratory research · 2023Article
- Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice.Nature protocols · 2023Review
- Semi-automated micro-computed tomography lung segmentation and analysis in mouse models.MethodsX · 2023Article
- A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.Respiratory research · 2022Article
- Lung Volume Calculation in Preclinical MicroCT: A Fast Geometrical Approach.Journal of imaging · 2022Article
- The importance of routine quality control for reproducible pulmonary measurements by in vivo micro-CT.Scientific reports · 2022Article
- An EZH2-dependent transcriptional complex promotes aberrant epithelial remodelling after injury.EMBO reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the main diagnostic tools for lung diseases in humans is computed tomography (CT). A miniaturized version, micro-CT (μCT) is utilized to examine small rodents including mice. However, fully automated threshold-based segmentation and subsequent quantification of severely damaged lungs requires visual inspection and manual correction.
methodsHere we demonstrate the use of densitometry on regions of interest (ROI) in automatically detected portions of the lung, thus avoiding the need for lung segmentation. Utilizing deep learning approaches, the middle part of the lung is found in a μCT-stack and a ROI is placed in the left and the right lobe.
resultsThe intensity values within the ROIs of the μCT images were collected and subsequently used for the calculation of different lung-related parameters, such as mean lung attenuation (MLA), mode, full width at half maximum (FWHM), and skewness. For validation, the densitometric approach was correlated with histological readouts (Ashcroft Score, Mean Linear Intercept).
conclusionWe here show an automated tool that allows rapid and in-depth analysis of μCT scans of different murine models of lung disease.
Indexed as
Identifiers
What OpenQuestion holds
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.