Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images

• Published in: Scientific reports 2024-02, Vol.14 (1), p.4393-4393, Article 4393
• Main Authors: Lee, Juhwan, Kim, Justin N., Dallan, Luis A. P., Zimin, Vladislav N., Hoori, Ammar, Hassani, Neda S., Makhlouf, Mohamed H. E., Guagliumi, Giulio, Bezerra, Hiram G., Wilson, David L.
• Format: Article
• Language: English
• Subjects: 639/166/985; 692/4019; 692/4019/2776; Automation; Calcification; Deep learning; Fibrous cap; Fibrous cap thickness; Humanities and Social Sciences; Image processing; Implants; Intravascular optical coherence tomography; multidisciplinary; Reproducibility; Risk factors; Science; Science (multidisciplinary); Segmentation; Sensitivity analysis; Thin-cap fibroatheroma; Tomography; Training; Transfer learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-55120-7

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT ( r,θ ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.