ArticleEuropean radiology experimental2020
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem.
Article in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 241 papers, 2 of them syntheses that pooled 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.
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
241 citing papers in PubMed, 2 syntheses or guidelines pooled it, 564 citations in OpenAlex.
- Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.Biomedical engineering online · 2024Pooled it
- 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
- Causal AI-based clinical and radiomic analysis for optimizing patient selection in combined immunotherapy and SABR in early-stage NSCLC: a secondary analysis of the phase II I-SABR trial.Journal for immunotherapy of cancer · 2025Trial
- Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study.Critical care (London, England) · 2022Trial
- Predicting lung cancer survival with attention-based CT slices combination.Health information science and systems · 2026Article
- In search of truth: evaluating concordance of AI-based anatomy segmentation models.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A transfer learning-based hybrid deep- and machine-learning regression approach for predicting the postoperative pulmonary function.European radiology experimental · 2026Article
- ProtoSurv: prototype-guided adaptation of computed tomography foundation model for lung-cancer prognosis prediction.Visual computing for industry, biomedicine, and art · 2026Article
- CT-based deep foundation model for predicting immune checkpoint inhibitor-induced pneumonitis risk in lung cancer.Journal for immunotherapy of cancer · 2026Article
- Quantifying the Resolution Ceiling of Prototype-Based Few-Shot Segmentation on Sub-Centimetre Pulmonary Nodules: A Training-Free Geometric Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Integration of clinical variables and CT radiomics features in a nomogram for predicting interstitial lung disease in Sjögren's syndrome.Journal of thoracic disease · 2026Article
- Anatomy-resolved digital twin framework for personalized light dosimetry in lung therapies.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Characterizing chronic obstructive pulmonary disease using quantitative MRI biomarkers.The British journal of radiology · 2026Review
- 3-D ordinal regression CNN for opportunistic breast density grading on chest CT.Journal of translational medicine · 2026Article
- Whole-Lung CT Radiomics-Based Machine Learning Classification of Nontuberculous Mycobacterial Lung Disease Across Geographically Distinct Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- The predicted body weight equation overestimates lung sizes of female, critically ill patients: an analysis of randomized, controlled trials and real-world clinical data.Intensive care medicine · 2026Article
- Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.BMC medical imaging · 2026Article
- Clinical application of AI for lung lobe segmentation and functional quantification with SPECT/CT: open-source versus custom models.Scientific reports · 2026Article
- MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.Research square · 2026Article
- Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free imaging artificial intelligence study.JTCVS open · 2026Article
181 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
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAutomated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited.
methodsWe compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets.
resultsUsing different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024).
conclusionsThe accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.
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.