Near Real-Time Multi-Class Segmentation for Intravascular Optical Coherence Tomography using Knowledge Distillation

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ID: 326956
2026
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Abstract
Abstract 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 3,466 manually annotated and 137,961 pseudo-labeled frames after automated quality control. On 389 internal test frames, OCT-AID-lite achieved a forward-pass time of 0.10 seconds for a 540-frame pullback, compared with 24.22 seconds for OCT-AID model (p<0.01). Including pre- and post-processing, total processing time was 5.50 seconds for OCT-AID-lite, compared with 30.62 seconds 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. Conclusions OCT-AID-lite enables accurate OCT segmentation in near real-time, allowing efficient quantitative characterization of plaque and vessel structures.
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Authors Ruben van der Waerden, Rick Volleberg, Pierandrea Cancian, Joske van der Zande, Thijs Luttikholt, Xiaojin Gu, Leah Heil, Jan‐Quinten Mol, Kensuke Nishimiya, Tomasz Roleder, Clara I. Sánchez, Bram van Ginneken, Jos Thannhauser, Niels van Royen, Ivana Išgum, Simone Saitta
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag138
URL
https://doi.org/10.1093/ehjdh/ztag138
Keywords Keywords not found

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