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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-CMS 2 2 QCU-CMS; Leiden, University Medical Center, Leiden, The Netherlands. to automatically segment IVUS images in a user-friendly environment. [Display omitted] • Temporal context-based feature encoders for dynamic lumen border feature extraction. • A selective transformer scheme with recurrent U-Net for predictive IVUS segmentation. • Adversarial learning to enhance border prediction resembling human vision. • A temporal constraining and fusion mechanism for context aware result enhancement. • Framework integrated in IVUS analysis software being used in research and clinics. [ABSTRACT FROM AUTHOR]