A Unified Framework for Multi-Guidewire Endpoint Localization in Fluoroscopy Images.
IEEE Trans Biomed Eng
; 69(4): 1406-1416, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-34613905
ABSTRACT
OBJECTIVE:
In this paper, Keypoint Localization Region-based CNN (KL R-CNN) is proposed, which can simultaneously accomplish the guidewire detection and endpoint localization in a unified model.METHODS:
KL R-CNN modifies Mask R-CNN by replacing the mask branch with a novel keypoint localization branch. Besides, some settings of Mask R-CNN are also modified to generate the keypoint localization results at a higher detail level. At the same time, based on the existing metrics of Average Precision (AP) and Percentage of Correct Keypoints (PCK), a new metric named APPCK is proposed to evaluate the overall performance on the multi-guidewire endpoint localization task. Compared with existing metrics, APPCK is easy to use and its results are more intuitive.RESULTS:
Compared with existing methods, KL R-CNN has better performance when the threshold is loose, reaching a mean APPCK of 90.65% when the threshold is 9 pixels.CONCLUSION:
KL R-CNN achieves the state-of-the-art performance on the multi-guidewire endpoint localization task and has application potentials.SIGNIFICANCE:
KL R-CNN can achieve the localization of guidewire endpoints in fluoroscopy images, which is a prerequisite for computer-assisted percutaneous coronary intervention. KL R-CNN can also be extended to other multi-instrument localization tasks.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Intervenção Coronária Percutânea
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article