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A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment

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Author(s)
Su YangHyuck‐Jun YoonSeyed Jamaleddin Mostafavi YazdiJong‐Ha Lee
Keimyung Author(s)
Yoon, Hyuck JunLee, Jong Ha
Department
Dept. of Internal Medicine (내과학)
Dept. of Biomedical Engineering (의용공학과)
Journal Title
International journal of medical robotics and computer assisted surgery
Issued Date
2020
Volume
16
Issue
1
Keyword
cardiaccardiologyheartimage analysisvascular surgeryvessel
Abstract
Background:
Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification.

Methods:
The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method.

Results:
As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively.

Conclusions:
Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.
Keimyung Author(s)(Kor)
윤혁준
이종하
Publisher
School of Medicine (의과대학)
Citation
Su Yang et al. (2020). A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment. International journal of medical robotics and computer assisted surgery, 16(1), e2033. doi: 10.1002/rcs.2033
Type
Article
ISSN
1478-596X
Source
https://onlinelibrary.wiley.com/doi/full/10.1002/rcs.2033
DOI
10.1002/rcs.2033
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43113
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Biomedical Engineering (의용공학과)
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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