Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks

Lennart Bargsten; Katharina A. Riedl; Tobias Wissel; Fabian J. Brunner; Klaus Schaefers; Johanna Sprenger; Michael Grass; Moritz Seiffert; Stefan Blankenberg; Alexander Schlaefer

doi:10.1117/12.2580720

15 February 2021 Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks

Lennart Bargsten, Katharina A. Riedl, Tobias Wissel, Fabian J. Brunner, Klaus Schaefers, Johanna Sprenger, Michael Grass, Moritz Seiffert, Stefan Blankenberg, Alexander Schlaefer

Author Affiliations +

Proceedings Volume 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography; 1160204 (2021) https://doi.org/10.1117/12.2580720
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Automatic delineation of relevant structures in intravascular imaging can support percutaneous coronary interventions (PCIs), especially when dealing with rather demanding cases. We found three major error types which occur regularly when segmenting lumen and wall of morphologically complex vessels with convolutional neural networks (CNNs). In order to reduce these three error types, we developed three IVUS-specific methods which are able to improve generalizability of state-of-the-art CNNs for IVUS segmentation tasks. These methods are based on three concepts: speckle statistics, artery shape priors via independent component analysis (ICA) and the concentricity condition of lumen and vessel wall. We found that all three methods outperform the baseline. Since all three concepts can be readily transferred to intravascular optical coherence tomography (IVOCT), we expect these findings can support the segmentation of corresponding images as well.

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Lennart Bargsten, Katharina A. Riedl, Tobias Wissel, Fabian J. Brunner, Klaus Schaefers, Johanna Sprenger, Michael Grass, Moritz Seiffert, Stefan Blankenberg, and Alexander Schlaefer "Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks", Proc. SPIE 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography, 1160204 (15 February 2021); https://doi.org/10.1117/12.2580720

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