Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN.
Comput Math Methods Med
; 2020: 1793517, 2020.
Article
en En
| MEDLINE
| ID: mdl-32952597
An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the "You Only Look Once" version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de la Arteria Coronaria
/
Stents
/
Tomografía de Coherencia Óptica
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Math Methods Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos