DMNet-E: Enhanced dual-segmentation multi-decoder network strategy for advancing IVOCT plaque segmentation.

Abstract

Coronary artery disease (CAD) remains a leading cause of global mortality, where precise plaque characterization is crucial for risk-stratification and intervention planning. Intravascular optical coherence tomography (IVOCT) provides high-resolution visualization of vascular structures, yet interpretation challenges hinder its broad clinical adoption. Although previous automated plaque segmentation studies have shown promising performance, automated plaque quantification remains challenging because clinically relevant tissue components are sparse, anatomically nested, affected by imaging artifacts and dependent on boundary-derived measurements. Moreover, most artificial intelligence models are evaluated against expert image annotations rather than tissue-level reference standards. To address these challenges, we propose an Enhanced Dual-segmentation Multi-decoder Network (DMNet-E). DMNet combines a mutually-exclusive multi-class decoder for accurate segmentation of small structures and a parallel multi-label binary decoder to preserve topological integrity in overlapping areas. Two task-specific components, including PizzaMix data augmentation for geometry-aware mixed-sample augmentation and Lipid Boundary Regression (LBR) module to highlight TCFA, further improve segmentation accuracy and clinical relevance. Through these designs, the model can fully harness complementary supervision signals provided by multi-class, multi-label, and regression objectives. DMNet-E achieved DSC values of 0.850, 0.706, and 0.865 for fibrous, lipid, and calcified plaques, respectively. Comparative experiments and histopathological validation jointly confirm its effectiveness and clinical relevance. Moreover, comprehensive transfer experiments further demonstrate DMNet's generalization potential within related IVOCT segmentation tasks and across different backbone settings.