Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging

Abstract

Multi-modal intracoronary imaging (visualize the inner structure of coronary arteries) has been proved to have great ability to help the coronary disease diagnosis in recent studies. However, no reported success on detecting all clinically-valuable vessel borders in multi-modal image analysis, because of the varied environment where the vessels are located in, i.e. inconsistent image appearance and tissue morphologies. This challenges the multi-modal vessel border detection, which is difficultly addressed by the hand-engineering feature extraction in existing single-mode methods. We propose a context-aware inductive bias learning approach to enable the detection of three vessel borders in multi-modal intracoronary imaging, i.e. the lumen and media-adventitia borders in intravascular ultrasound (IVUS), and the lumen border in optical coherence tomography (OCT). Our approach exploits the detection process of one vessel border as a model constraint to another vessel border based on the vessel contextual information. It is specified by an elaborately-designed semantic-fusion multi-task neural network, which exploits the similarly semantic information and different environment information to lead the mutual regularization among different vessel border detection tasks. The extensive experiments show the effectiveness of our approach by the highly overlapping degree with the ground truth and the superiority to six state-of-the-art single-mode vessel border detection methods.