Artículo
Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs
Fecha de publicación:
08/2019
Editorial:
Springer
Revista:
International Journal of Computer Assisted Radiology and Surgery
ISSN:
1861-6410
e-ISSN:
1861-6429
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Purpose: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans. Methods: A ray-casting US simulation approach is used to generate intermediate synthetic images from abdominal CT scans. Then, an unpaired set of these synthetic and real US images is used to train CycleGANs with two alternative architectures for the generator, a U-Net and a ResNet. These networks are finally used to translate ray-casting based simulations into more realistic synthetic US images. Results: Our approach was evaluated both qualitatively and quantitatively. A user study performed by 21 experts in US imaging shows that both networks significantly improve realism with respect to the original ray-casting algorithm (p≪ 0.0001), with the ResNet model performing better than the U-Net (p≪ 0.0001). Conclusion: Applying CycleGANs allows to obtain better synthetic US images of the abdomen. These results can contribute to reduce the gap between artificially generated and real US scans, which might positively impact in applications such as semi-supervised training of machine learning algorithms and low-cost training of medical doctors and radiologists in US image interpretation.
Palabras clave:
DEEP LEARNING
,
IMAGE SIMULATION
,
ULTRASOUND
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(CCT - TANDIL)
Articulos de CTRO CIENTIFICO TECNOLOGICO CONICET - TANDIL
Articulos de CTRO CIENTIFICO TECNOLOGICO CONICET - TANDIL
Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Vitale, Santiago; Orlando, José Ignacio; Iarussi, Emmanuel; Larrabide, Ignacio; Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs; Springer; International Journal of Computer Assisted Radiology and Surgery; 15; 2; 8-2019; 183-192
Compartir
Altmétricas