Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures

TY - CHAP

T1 - Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures

AU - Ma, Ying Liang

AU - Mehta, Vishal S.

AU - Rinaldi, C. Aldo

AU - Hu, Pengpeng

AU - Niederer, Steven

AU - Razavi, Reza

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - The lead extraction procedures are for the patients who already have pacemaker implanted and leads need to be replaced. The procedure is a high-risk procedure and it could lead to major complications or even procedure-related death. Recently, an Electra Registry Outcome Score (EROS) was designed to create a risk assessment tool using the data about personal health records and an accuracy of 0.70 was achieved. In this paper, we hypothesized that a coil inside the superior vena cava (SVC) is a very important risk factor. By integrating it into the risk assessment model, the accuracy can be further improved. Therefore, an automatic detection method was developed to localize the positions of coils in the X-ray images. It was based on a U-Net convolutional network. To determine the coil position relative to the SVC position inside the chest X-ray image, the heart region was first detected by using a modified VGG16 model. Then, the bounding box of the SVC can be estimated based on the heart anatomy. Finally, a XGBoost classifier was trained on the data about personal health records and the risk factor about the coil position. An accuracy of 0.85 was achieved.

AB - The lead extraction procedures are for the patients who already have pacemaker implanted and leads need to be replaced. The procedure is a high-risk procedure and it could lead to major complications or even procedure-related death. Recently, an Electra Registry Outcome Score (EROS) was designed to create a risk assessment tool using the data about personal health records and an accuracy of 0.70 was achieved. In this paper, we hypothesized that a coil inside the superior vena cava (SVC) is a very important risk factor. By integrating it into the risk assessment model, the accuracy can be further improved. Therefore, an automatic detection method was developed to localize the positions of coils in the X-ray images. It was based on a U-Net convolutional network. To determine the coil position relative to the SVC position inside the chest X-ray image, the heart region was first detected by using a modified VGG16 model. Then, the bounding box of the SVC can be estimated based on the heart anatomy. Finally, a XGBoost classifier was trained on the data about personal health records and the risk factor about the coil position. An accuracy of 0.85 was achieved.

KW - Deep learning

KW - Risk assessment

KW - Wire detection

UR - http://www.scopus.com/inward/record.url?scp=85172735021&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-35302-4_32

DO - 10.1007/978-3-031-35302-4_32

M3 - Conference paper

AN - SCOPUS:85172735021

SN - 9783031353017

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 310

EP - 319

BT - Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

A2 - Bernard, Olivier

A2 - Clarysse, Patrick

A2 - Duchateau, Nicolas

A2 - Ohayon, Jacques

A2 - Viallon, Magalie

PB - Springer Science and Business Media Deutschland GmbH

T2 - Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

Y2 - 19 June 2023 through 22 June 2023

ER -