| [157010] |
| Title: Capsule networks for segmentation of small intravascular ultrasound image datasets. |
| Written by: L. Bargsten and S. Raschka and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. (2021). |
| Volume: <strong>16</strong>. Number: (8), |
| on pages: 1243-1254 |
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| DOI: 10.1007/s11548-021-02417-x |
| URL: https://doi.org/10.1007/s11548-021-02417-x |
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Abstract: Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks.