Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions

Abstract : Purpose Quantitative and automatic analysis of intracoro-nary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. Methods First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. Results The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were 29 ± 46 µm for the intima-media interface, 30 ± 50 µm for the media-adventitia interface, and 50 ± 64 µm for the adventitia-periadventitia interface. The classification B Guillaume Zahnd method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98). Conclusion The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summa-rization of healthy regions in entire pullbacks.
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Guillaume Zahnd, Ayla Hoogendoorn, Nicolas Combaret, Antonios Karanasos, Emilie Pery, et al.. Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2017, 12 (11), pp.1923-1936. ⟨10.1007/s11548-017-1657-7⟩. ⟨hal-01654850⟩

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