POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS1 to automatically segment IVUS images in a user-friendly environment.

Automated summary

In this paper, a novel perceptual organisation-aware selective transformer framework is proposed for accurate and robust segmentation of vessel walls in IVUS images. The framework utilizes temporal context-based feature encoders to extract efficient motion features of vessels and incorporates a perceptual organisation-aware selective transformer module to extract accurate boundary information. The obtained results are fused in a temporal constraining and fusion module to determine the most likely correct boundaries with robustness to morphology. Extensive evaluation on non-selected IVUS sequences demonstrates that the proposed methods outperform the state-of-the-art. The work has been integrated into a user-friendly software for automatic IVUS image segmentation.