ArticleBiomedical physics & engineering express2019
Automatic diaphragm segmentation for real-time lung tumor tracking on cone-beam CT projections: a convolutional neural network approach.
Article in Biomedical physics & engineering express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fully automated diaphragm segmentation on routine 2D-cine and 3D thoracic MRI: multireader validation.European radiology experimental · 2026Article
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- Patient-specific prostate segmentation in kilovoltage images for radiation therapy intrafraction monitoring via deep learning.Communications medicine · 2025Article
- Artificial intelligence-based motion tracking in cancer radiotherapy: A review.Journal of applied clinical medical physics · 2024Review
- Deep learning in structural and functional lung image analysis.The British journal of radiology · 2022Review
- The markerless lung target tracking AAPM Grand Challenge (MATCH) results.Medical physics · 2022Article
- Lung tumor segmentation in 4D CT images using motion convolutional neural networks.Medical physics · 2021Article
- Comparative Evaluation of Conventional and Deep Learning Methods for Respiratory Signal Extraction From Clinical 3D CBCT Projections.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeTo automatically segment the diaphragm on individual lung cone-beam CT projection images, to enable real-time tracking of lung tumors using kilovoltage imaging.
methodsThe deep neural network Mask R-CNN was trained on 3500 raw cone-beam CT projection images from 10 lung cancer patients, with the diaphragm manually segmented on each image used as a ground truth label. Ground-truth breathing traces were extracted from each patient for both diaphragm hemispheres, and apex positions were compared against the predicted output of the neural network. Ten-fold cross-validation was used to evaluate the segmentation accuracy.
resultsThe mean diaphragm apex prediction error was 4.4 mm. The mean percentage of projection images for which a successful prediction could me made was 87.3%. Prediction accuracy at some lateral gantry angles was worse due to overlap between diaphragm hemispheres, and the increased amount of fatty tissue.
conclusionsThe neural network was able to track the diaphragm apex position successfully. This allows accurate assessment of the breathing phase, which can be used to estimate the position of the lung tumor in real time.
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