Multi-class plaque segmentation in Intravascular Ultrasound via inter-frame feature fusion and contrast feature extraction

Abstract

Intravascular Ultrasound (IVUS) is an effective aid in diagnosing cardiovascular diseases, with plaque segmentation in IVUS images being a pivotal step in assessing vascular plaque risk. However, IVUS plaque segmentation encounters two major challenges: (1) the variability in plaque shapes makes it difficult to locate plaques accurately, and (2) distinguishing plaque types is challenging due to the similarity in visual features among different types. To tackle these challenges, we propose a novel framework for IVUS plaque segmentation. Our approach incorporates a segmentation network featuring a dual feature attention(DFA) module and a cross feature fusion(CFF) module. Additionally, we introduce contrastive learning to enhance plaque sensitivity and extract contrast features within our network. We evaluate our method using a private IVUS dataset, achieving the state-of-the-art performance with a Dice score of 84.79% and a 14.17 HD value. Our approach represents significant progress in the field of multi-class IVUS plaque segmentation, making it a more effective tool to assist doctors in diagnosing vascular plaques.