A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment. Issue 1 (13th November 2019)

• Record Type: Journal Article
• Authors: Yang, Su; Yoon, Hyuck‐Jun; Yazdi, Seyed Jamaleddin Mostafavi; Lee, Jong‐Ha
• Is Part Of: International journal of medical robotics and computer assisted surgery. Volume 16:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-13
• Subjects: cardiac -- cardiology -- heart -- image analysis -- vascular surgery -- vessel; Robotics in medicine -- Periodicals; Surgery -- Technological innovations -- Periodicals; Imaging systems in medicine -- Periodicals; 617.90285
• DOI: 10.1002/rcs.2033
• Languages: English
• ISSNs: 1478-5951

Abstract

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.