A multiple catheter tips tracking method in X-ray fluoroscopy images by a new lightweight segmentation network and Bayesian filtering.
X-ray fluoroscopy sequence
catheter tips segmentation network
multi-objective Bayesian filtering
self-distillation training
Journal
The international journal of medical robotics + computer assisted surgery : MRCAS
ISSN: 1478-596X
Titre abrégé: Int J Med Robot
Pays: England
ID NLM: 101250764
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
revised:
29
07
2023
received:
14
06
2023
accepted:
17
08
2023
medline:
6
11
2023
pubmed:
27
8
2023
entrez:
27
8
2023
Statut:
ppublish
Résumé
During percutaneous coronary intervention, the guiding catheter plays an important role. Tracking the catheter tip placed at the coronary ostium in the X-ray fluoroscopy sequence can obtain image displacement information caused by the heart beating, which can help dynamic coronary roadmap overlap on X-ray fluoroscopy images. Due to a low exposure dose, the X-ray fluoroscopy is noisy and low contrast, which causes some difficulties in tracking. In this paper, we developed a new catheter tip tracking framework. First, a lightweight efficient catheter tip segmentation network is proposed and boosted by a self-distillation training mechanism. Then, the Bayesian filtering post-processing method is used to consider the sequence information to refine the single image segmentation results. By separating the segmentation results into several groups based on connectivity, our framework can track multiple catheter tips. The proposed tracking framework is validated on a clinical X-ray sequence dataset.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2569Subventions
Organisme : National Key Research and Development Program of China
ID : 2018YFA0704102
Organisme : National Key Research and Development Program of China
ID : 2018YFA0704104
Organisme : National Key Research and Development Program of China
ID : 2022YFC2408500
Organisme : National Natural Science Foundation of China
ID : 81827805
Organisme : Basic Research Project of Shenzhen Science and Technology Innovation Commission
ID : JCYJ20200109114610201
Organisme : Key Research and Development Programs in Jiangsu Province of China
ID : BE2021703
Organisme : Key Research and Development Programs in Jiangsu Province of China
ID : BE2022768
Organisme : Natural Science Foundation of Guangdong Province
ID : 2023A1515010673
Organisme : the Shenzhen Engineering Laboratory for Diagnosis & Treatment Key Technologies of Interventional Surgical Robots
ID : XMHT20220104009
Organisme : Shenzhen Science and Technology
ID : JSGG20220831110400001
Informations de copyright
© 2023 John Wiley & Sons Ltd.
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