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

Abstract

PurposeQuantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena.MethodsFirst, 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.ResultsThe 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 for the intima–media interface, for the media–adventitia interface, and for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98).ConclusionThe 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 summarization of healthy regions in entire pullbacks.

A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment

“…In previous studies, IVOCT classification studies using machine learning focused on classification of vessel layer or vulnerable plaque . In this study, we focused on classification of abnormal lumen that includes intra‐luminal atherothrombotic material (protrusion) by handcrafted features.…”

“…Finally, Zahnd et al developed classification method to identify healthy and diseased region of arterial wall. A set of 17 tentative features were extracted from each column of segmented region for the classification.…”

Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

“…These methods include the use of the local maximum OCT signal intensity, local maximum gradient of the OCT signal, and the Canny edge detector. 70,[110][111][112] The underlying tissue may present different morphologies and need further segmentation, especially when the tissue presents layered structures with varying OCT signal strength. For example, normal skin comprises epidermis (low signal) and dermis (high signal), and normal coronary arterial wall comprises intima (high signal), media (low signal), and adventitia (high signal), which are further segmented to confine the depth ranges for attenuation measurement.…”

“…For example, normal skin comprises epidermis (low signal) and dermis (high signal), and normal coronary arterial wall comprises intima (high signal), media (low signal), and adventitia (high signal), which are further segmented to confine the depth ranges for attenuation measurement. 112,113 Overall, the capacity to locate homogeneous tissue regions for attenuation calculation is dependent on the specific tissue morphology and the corresponding contrast in OCT signal and may need additional revision of the methods under specific disease conditions. Such revisions will be disease-and tissue-dependent and may not be feasible for all conditions.…”

Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights

“…Studies on the segmentation of endoscopic OCT images are not as extensive as the macular ones. Representative researches can be found in the processing of cardiovascular [ 28 – 30 ] and esophageal OCT images [ 31 – 36 ]. As reported, the graph based method is also effective in segmenting cardiovascular [ 30 ] and esophageal tissue layers [ 36 ].…”