ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2021
LGAN: Lung segmentation in CT scans using generative adversarial network.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 88 citations in OpenAlex.
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- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Accuracy assessment of a hybrid deep learning and image processing approach for lung CT segmentation in external radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.PloS one · 2024Article
- Lung image segmentation via generative adversarial networks.Frontiers in physiology · 2024Article
- Segmentation of glioblastomas via 3D FusionNet.Frontiers in oncology · 2024Article
- Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.Annals of biomedical engineering · 2023Review
- A hierarchical GAN method with ensemble CNN for accurate nodule detection.International journal of computer assisted radiology and surgery · 2023Article
- The Influence of a Coherent Annotation and Synthetic Addition of Lung Nodules for Lung Segmentation in CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet.Computational and mathematical methods in medicine · 2022Review
- A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images.EURASIP journal on advances in signal processing · 2022Article
- AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging.PloS one · 2022Article
- Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey.International journal of multimedia information retrieval · 2022Review
- Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip.Frontiers in bioengineering and biotechnology · 2022Review
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Robust and accurate pulmonary nodule detection with self-supervised feature learning on domain adaptation.Frontiers in radiology · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing an automatic segmentation method with fewer steps, we propose a novel deep learning Generative Adversarial Network (GAN)-based lung segmentation schema, which we denote as LGAN. The proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images. We evaluated the proposed LGAN schema on datasets including Lung Image Database Consortium image collection (LIDC-IDRI) and Quantitative Imaging Network (QIN) collection with two metrics: segmentation quality and shape similarity. Also, we compared our work with current state-of-the-art methods. The experimental results demonstrated that the proposed LGAN schema can be used as a promising tool for automatic lung segmentation due to its simplified procedure as well as its improved performance and efficiency.
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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.