Near real-time multi-class segmentation for intravascular optical coherence tomography using knowledge distillation.

Aims: Intravascular optical coherence tomography (OCT) enables high-resolution imaging of the coronary vessel wall, but manual image interpretation is time-consuming and existing automated approaches often require high computational resources and exhibit slow inference times, limiting clinical use. We developed OCT-AID-lite, a neural network for near-real-time multi-class OCT segmentation leveraging knowledge distillation and semi-supervised learning to accelerate inference while maintaining segmentation accuracy. Methods and results: A state-of-the-art model (OCT-AID) guided a compact U-Net-based student model (OCT-AID-lite) through knowledge distillation-based supervision. OCT-AID-lite was trained on 3466 manually annotated and 137 961 pseudo-labelled frames after automated quality control. On 389 internal test frames, OCT-AID-lite achieved a forward-pass time of 0.10 s for a 540-frame pullback, compared with 24.22 s for the OCT-AID model ( Conclusion: OCT-AID-lite enables accurate OCT segmentation in near real-time, allowing efficient quantitative characterization of plaque and vessel structures.