| [153352] |
| Title: Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks. <em>Medical Imaging 2021: Ultrasonic Imaging and Tomography</em> |
| Written by: L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and J. Sprenger and M. Grass and M. Seiffert and S. Blankenberg and A. Schlaefer |
| in: (2021). |
| Volume: <strong>11602</strong>. Number: |
| on pages: 1-7 |
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| Editor: In Brett C. Byram and Nicole V. Ruiter (Eds.) |
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| Organization: International Society for Optics and Photonics |
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| DOI: 10.1117/12.2580720 |
| URL: https://doi.org/10.1117/12.2580720 |
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Note:
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