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

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.

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 (P  <  0.01). Including pre- and post-processing, total processing time was 5.50 s for OCT-AID-lite, compared with 30.62 s for OCT-AID (P  <  0.01). For lipid and calcium plaque classification, OCT-AID-lite reached sensitivity/specificity of 98.1%/74.4% and 89.5%/87.5%, respectively. Pixel-wise segmentation performance on true-positive frames was high for guidewire, catheter, lumen, intima, and media (Dice: 0.79–0.99), moderate to high for sidebranch, lipid, and calcium (Dice: 0.76–0.78), and more variable for rare complex classes (Dice: 0.39–0.67). On an independent external test set, model predictions were in agreement with expert assessment.

OCT-AID-lite enables accurate OCT segmentation in near real-time, allowing efficient quantitative characterization of plaque and vessel structures.