Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks.
Annu Int Conf IEEE Eng Med Biol Soc
; 2018: 2599-2602, 2018 Jul.
Article
en En
| MEDLINE
| ID: mdl-30440940
ABSTRACT
Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes. We demonstrate the feasibility of the proposed approach quantitatively and qualitatively by segmenting relevant fetal structures to assess the anatomical fidelity of the simulation, and performing a clinical verisimilitude study distinguishing the simulated data from the real images. The results obtained so far are promising, which makes further investigation on this new topic worthwhile.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Imagen por Resonancia Magnética
/
Aprendizaje Automático
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2018
Tipo del documento:
Article