ArticleRadiological physics and technology2021
Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs.
Article in Radiological physics and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Pooled it
- Consensus approach to shape regularization in cross-modality cardiac segmentation.Radiological physics and technology · 2026Article
- Streamlining the annotation process by radiologists of volumetric medical images with few-shot learning.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning-based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia.Journal of neurosurgery · 2025Article
- Automated Euler number of the alveolar capillary network based on deep learning segmentation with verification by stereological methods.Journal of microscopy · 2025Article
- Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.Radiological physics and technology · 2025Article
- Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.Journal of applied clinical medical physics · 2024Article
- Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation.Physical and engineering sciences in medicine · 2024Article
- Diagnosis of ophthalmologic diseases in canines based on images using neural networks for image segmentation.Heliyon · 2024Article
- Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.Journal of radiation research · 2024Review
- AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
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- Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: A longitudinal study on iron accumulation with age.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.BJR open · 2024Review
- Age estimation based on 3D pulp segmentation of first molars from CBCT images using U-Net.Dento maxillo facial radiology · 2023Article
- Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.Bioengineering (Basel, Switzerland) · 2023Article
- Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.PloS one · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can be challenging. This study aims to elucidate the effects of sample size and data augmentation on the automatic segmentation of computed tomography images using U-Net, a deep learning method. For the chest and pelvic regions, 232 and 556 cases are evaluated, respectively. We investigate multiple conditions by changing the sum of the training and validation datasets across a broad range of values: 10-200 and 10-500 cases for the chest and pelvic regions, respectively. A U-Net is constructed, and horizontal-flip data augmentation, which produces left and right inverse images resulting in twice the number of images, is compared with no augmentation for each training session. All lung cases and more than 100 prostate, bladder, and rectum cases indicate that adding horizontal-flip data augmentation is almost as effective as doubling the number of cases. The slope of the Dice similarity coefficient (DSC) in all organs decreases rapidly until approximately 100 cases, stabilizes after 200 cases, and shows minimal changes as the number of cases is increased further. The DSCs stabilize at a smaller sample size with the incorporation of data augmentation in all organs except the heart. This finding is applicable to the automation of radiation therapy for rare cancers, where large datasets may be difficult to obtain.
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