SynthesisYearbook of medical informatics2022
A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.
Synthesis in Yearbook of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.Research square · 2026Article
- LobePrior segments lung lobes on computed tomography images in the presence of severe abnormalities.Scientific reports · 2026Article
- Unsupervised segmentation of dynamic pulmonary MRI using cross-modality adaptation with annotated CT images.International journal of computer assisted radiology and surgery · 2026Article
- Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA.NPJ digital medicine · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- Lung lobe segmentation: performance of open-source MOOSE, TotalSegmentator, and LungMask models compared to a local in-house model.European radiology experimental · 2025Article
- A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation.Journal of applied clinical medical physics · 2025Article
- AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.Scientific reports · 2025Article
- Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators.Scientific data · 2025Article
- TriSwinUNETR lobe segmentation model for computing DIR-free CT-ventilation.Frontiers in oncology · 2025Article
- Real-world federated learning in radiology: hurdles to overcome and benefits to gain.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Imaging the Lung in ARDS: A Primer.Respiratory care · 2024Review
- Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- GPU-accelerated lung CT segmentation based on level sets and texture analysis.Scientific reports · 2024Article
- Inclusive Digital Health.Yearbook of medical informatics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAutomated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.
methodsWe used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation.
resultsWe organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field.
conclusionsWe noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.
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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.