Abstract:
Intracoronary imaging helps diagnose coronary disease by showing coronary artery tissue morphologies. Computer-aided disease diagnosis can benefit from intracoronary vessel border detection (VBDI).
Existing VDBI methods struggle with vessel-environment variability (high intra- and inter-subject diversity of vessels and their surrounding tissues in images). This challenge causes ineffective vessel region representation for hand-crafted features, receptive field extraction for deeply-represented features, and performance suppression from clinical data limitation.
We propose a new VBDI PMD framework to address this issue. PMD uses the privileged image modality to help the target modality learning model convert the single-input-single-task (SIST) learning problem in the single-mode VBDI to a MIMT problem.
This learns enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features to improve model adaptation to diverse vessel environments. PMD also simplifies MIMT to SIST by consolidating learned knowledge.
This eliminates privileged modality in the test phase and allows applicability to all intracoronary modalities. An elaborate PMD implementation is a structure-deformable neural network. It expands a conventional SIST network structure to MIMT and then recovers it to the final SIST structure.
OCT and intravascular ultrasound validate the PMD. The target modality is semantically related to the privileged modality. Our PMD outperforms six top VBDI methods (Dice index > 0.95) in experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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