Abstract
We present a method for automatic struts detection and stent shape estimation in cross-sectional intravascular ultrasound images. A stent shape is first estimated through a comprehensive interpretation of the vessel morphology, performed using a supervised context-aware multi-class classification scheme. Then, the successive strut identification exploits both local appearance and the defined stent shape. The method is tested on 589 images obtained from 80 patients, achieving a F-measure of 74.1% and an averaged distance between manual and automatic struts of 0.10 mm.
This work was supported in part by the MICINN Grants TIN2009-14404-C02.
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Ciompi, F., Balocco, S., Caus, C., Mauri, J., Radeva, P. (2013). Stent Shape Estimation through a Comprehensive Interpretation of Intravascular Ultrasound Images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_43
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DOI: https://doi.org/10.1007/978-3-642-40763-5_43
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