Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?
Z Med Phys
; 32(3): 361-368, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-34930685
ABSTRACT
PURPOSE:
For 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/METHODS:
Two 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.RESULTS:
No 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.CONCLUSION:
When introducing GANs for the segmentation task no significant performance boost was observed in our experiments, even in segmentation models developed on small datasets.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article