ReviewRadiographics : a review publication of the Radiological Society of North America, Inc
Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.
Review in Radiographics : a review publication of the Radiological Society of North America, Inc. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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.
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Who cites it
62 citing papers in PubMed.
- A Comparative Evaluation of Zero-Shot Performance of SAM, SAM2, MedSAM, and MedSAM2 Models on Lung Segmentation.Journal of imaging informatics in medicine · 2026Article
- Comparison of multi-organ CT image segmentation tools for whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Development and Radiological Evaluation of 3D-Printed Patient-Specific Lung Phantoms: From CT Imaging to 3D Modeling and Material Characterization.Research square · 2026Article
- Semi-Automated Lung Segmentation Based on Region-Growing Methods in Interstitial Lung Disease.Journal of clinical medicine · 2026Article
- A concept for fully automated segmentation of bone in ultrasound imaging.Scientific reports · 2025Article
- BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.Journal of anatomy · 2024Article
- Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data.IEEE transactions on bio-medical engineering · 2024Article
- ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection.Heliyon · 2024Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- GPU-accelerated lung CT segmentation based on level sets and texture analysis.Scientific reports · 2024Article
- Article
- Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network.Frontiers in veterinary science · 2024Article
- Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.Journal of magnetic resonance imaging : JMRI · 2023Article
- Review
- Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets.Heliyon · 2023Article
- A hierarchical GAN method with ensemble CNN for accurate nodule detection.International journal of computer assisted radiology and surgery · 2023Article
- Review
- Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study.Frontiers in immunology · 2023Article
- Automated semantic lung segmentation in chest CT images using deep neural network.Neural computing & applications · 2023Article
- Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.
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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.