A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment
- Affiliated Author(s)
- 윤혁준; 이종하
- Alternative Author(s)
- Yoon, Hyuck Jun; Lee, Jong Ha
- Journal Title
- International journal of medical robotics and computer assisted surgery
- ISSN
- 1478-596X
- Issued Date
- 2020
- Keyword
- cardiac; cardiology; heart; image analysis; vascular surgery; vessel
- 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.
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