ArticleNuclear medicine and molecular imaging2023
Automatic Lung Cancer Segmentation in [
Article in Nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
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- Physician-guided deep learning model for assessing thymic epithelial tumor volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- 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
- Hypoxia Imaging in Lung Cancer: A PET-Based Narrative Review for Clinicians and Researchers.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.Nature communications · 2025Article
- A comprehensive review on Compton camera image reconstruction: from principles to AI innovations.Biomedical engineering letters · 2024Review
- Recent Updates of PET in Lymphoma: FDG and Beyond.Biomedicines · 2024Review
- Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT.Journal of clinical medicine · 2024Article
- Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizingBMC cancer · 2024Article
- An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.Journal of imaging informatics in medicine · 2024Article
- Deep learning-based measurement of split glomerular filtration rate withEJNMMI physics · 2024Article
- Article
- Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level.Translational lung cancer research · 2024Article
- A CT-based deep learning model for preoperative prediction of spread through air spaces in clinical stage I lung adenocarcinoma.Frontiers in oncology · 2024Article
- ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: Bridging the AI Gap for Clinicians.Heliyon · 2023Article
- Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of IntraoperativeDiagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [ Methods: The whole-body [ Results: The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. Conclusion: The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [
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Registered trials
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