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Capsule networks for segmentation of small intravascular ultrasound image datasets
Citation Link: https://doi.org/10.15480/882.3821
Publikationstyp
Journal Article
Date Issued
2021-06-14
Sprache
English
Author(s)
TORE-DOI
TORE-URI
Volume
16
Issue
8
Start Page
1243
End Page
1254
Citation
International Journal of Computer Assisted Radiology and Surgery 16 (8): 1243–1254 (2021)
Publisher DOI
Scopus ID
Publisher
Springer
Purpose: 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. Methods: We systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters. Results: Contrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance. Conclusion: Capsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.
Subjects
Capsule networks
Deep learning
Image segmentation
Intravascular ultrasound
Small datasets
DDC Class
004: Informatik
600: Technik
610: Medizin
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