PricoMS: Prior-coordinated multiscale synthesis network for self-supervised–aided vessel segmentation in intravascular ultrasound image amidst label scarcity

Intravascular ultrasound (IVUS) imaging is invaluable in aiding diagnosis and intervention of coronary artery disease. However its use is limited because of the increased time needed to segment the IVUS images and accurately quantify plaque burden, and lesion severity. To overcome this limitation we present a prior-coordinated multiscale synthesis network (PricoMS) for segmenting IVUS images under the condition of label scarcity. This network integrates a prior coherence paradigm (PCP), which enhances structural synthesis by maintaining consistency across scales, and a hierarchical contextual synthesis (HCS) module, which facilitates the integration of contextual information for better spatial understanding. To address the challenge of label scarcity in IVUS data, a prior encoder repeatedly utilizes unlabeled IVUS images for training, providing prior features of the images for segmentation tasks. Additionally, this network employs an adaptive morphological fusion-contextual space encoding (AMF-CSE) module to capture multi-scale and contextual data, thereby bolstering the model†™s capability to discern intricate vascular features even in challenging areas with suboptimal quality and imaging artifacts such as electronic noise, speckle noise, motion artifacts, and acoustic scattering. PricoMS exhibits robust performance, achieving a Dice score of 95.2% for detecting the lumen border and 84.0% for detecting the external elastic membrane (EEM) border, surpassing many existing techniques. The source code is publicly accessible at: https://github.com/IMOP-lab/PricoMS-Pytorch.