HoliMAb: A holistic approach for Media–Adventitia border detection in intravascular ultrasound

We present a fully automatic methodology for the detection of the Media–Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed.

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► We present a framework for automatic detection of Media–Adventitia border in IVUS.
► Tissue classification is applied through a context-aware multi-class classifier.
► A robust border detection is obtained thanks to a holistic interpretation of vessel morphology.
► The error is comparable with inter-observer variability on two challenging databases.
► The method equals state-of-the-art and improve some of the error parameters.