European heart journal. Digital health · 2026
Near real-time multi-class segmentation for intravascular optical coherence tomography using knowledge distillation.
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van der Waerden Ruben G A, Volleberg Rick H J A, Cancian Pierandrea, van der Zande Joske L, Luttikholt Thijs J, Gu Xiaojin, Heil Leah, Mol Jan-Quinten, Nishimiya Kensuke, Roleder Tomasz, Sánchez Clara I, van Ginneken Bram, Thannhauser Jos, van Royen Niels, Išgum Ivana, Saitta Simone
Аннотация
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 (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.
CONCLUSION: OCT-AID-lite enables accurate OCT segmentation in near real-time, allowing efficient quantitative characterization of plaque and vessel structures.
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 (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.
CONCLUSION: OCT-AID-lite enables accurate OCT segmentation in near real-time, allowing efficient quantitative characterization of plaque and vessel structures.