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A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment
Author(s) -
Yang Su,
Yoon HyuckJun,
Yazdi Seyed Jamaleddin Mostafavi,
Lee JongHa
Publication year - 2020
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2033
Subject(s) - lumen (anatomy) , segmentation , computer science , artificial intelligence , stent , partition (number theory) , pattern recognition (psychology) , medicine , radiology , mathematics , surgery , combinatorics
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.