Multi-object segmentation of anatomical layers in intravascular polarization-sensitive optical coherence tomography using convolutional neural networks

Mohammad Haft-Javaherian; Kenichiro Otsuka; Peter Libby; Polina Golland; Martin Villiger; Brett E. Bouma

doi:10.1117/12.2578851

5 March 2021 Multi-object segmentation of anatomical layers in intravascular polarization-sensitive optical coherence tomography using convolutional neural networks

Mohammad Haft-Javaherian, Kenichiro Otsuka, Peter Libby, Polina Golland, Martin Villiger, Brett E. Bouma

Author Affiliations +

Proceedings Volume 11621, Diagnostic and Therapeutic Applications of Light in Cardiology 2021; 116210E (2021) https://doi.org/10.1117/12.2578851
Event: SPIE BiOS, 2021, Online Only

Abstract

Intravascular polarimetry complements the high-resolution images of conventional intravascular optical coherence tomography (OCT) with quantitative measures of tissue polarization properties that relate to tissue composition. Yet, additional metrics further complicate image interpretation, and leveraging the quantitative polarization metrics currently relies on tedious manual segmentation. Here, we developed a customized convolutional neural network multi-class segmentation model to detect the intima-media boundary whenever visible and otherwise identify the presence of attenuating plaque that masks this boundary. Combined with the IV-OCT backscatter signal, the polarization metrics that enhance the discrimination of targeted features, thereby improving the accuracy and robustness of automated segmentation and lesion identification.

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Mohammad Haft-Javaherian, Kenichiro Otsuka, Peter Libby, Polina Golland, Martin Villiger, and Brett E. Bouma "Multi-object segmentation of anatomical layers in intravascular polarization-sensitive optical coherence tomography using convolutional neural networks", Proc. SPIE 11621, Diagnostic and Therapeutic Applications of Light in Cardiology 2021, 116210E (5 March 2021); https://doi.org/10.1117/12.2578851

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