ArticleZeitschrift fur medizinische Physik2022
Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?
Article in Zeitschrift fur medizinische Physik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing clinical safety in radiation oncology: A data-driven approach to risk management.Zeitschrift fur medizinische Physik · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Article
- Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application.Journal of imaging informatics in medicine · 2024Article
- Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.Zeitschrift fur medizinische Physik · 2024Article
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy.Physics and imaging in radiation oncology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFor image translational tasks, the application of deep learning methods showed that Generative Adversarial Network (GAN) architectures outperform the traditional U-Net networks, when using the same training data size. This study investigates whether this performance boost can also be expected for segmentation tasks with small training dataset size. MATERIALS/
methodsTwo models were trained on varying training dataset sizes ranging from 1-100 patients: a) U-Net and b) U-Net with patch discriminator (conditional GAN). The performance of both models to segment the male pelvis on CT-data was evaluated (Dice similarity coefficient, Hausdorff) with respect to training data size.
resultsNo significant differences were observed between the U-Net and cGAN when the models were trained with the same training sizes up to 100 patients. The training dataset size had a significant impact on the models' performances, with vast improvements when increasing dataset sizes from 1 to 20 patients.
conclusionWhen introducing GANs for the segmentation task no significant performance boost was observed in our experiments, even in segmentation models developed on small datasets.
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