Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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G. Zahnd, A. Karanasos, G. van Soest, E. Regar, W. Niessen, F. Gijsen, T. van Walsum
Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
International Journal of Computer Assisted Radiology and Surgery (bib)

Objectives. Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and peri-operative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods. A novel semi-automatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness, namely the diffuse appearance of the anatomical abluminal interface to be detected, the proposed framework is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameters settings, a training phase was conducted on 10 patients. Results. Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22±18 µm, R=.73) and was similar to inter-observer reproducibility (21±19 µm, R=.74), while being significantly faster and fully reproducible. Conclusion. The proposed framework demonstrated promising performances and could potentially be used for online identification of high risk-plaques.
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