Abstract
Coronary artery segmentation on angiograms can be beneficial in the diagnosis and treatment of coronary artery diseases. In this paper, we propose a self-supervised vessel segmentation framework that incorporates the knowledge from generated digitally reconstructed radiographs(DRRs) to perform vessel segmentation on angiographic images without manual annotations. The framework is built based on domain randomization, adversarial learning, and self-supervised learning. Domain randomization and adversarial learning are able to effectively reduce the domain gaps between DRRs and angiograms, whereas self-supervised learning enables the network to learn photometric invariant and geometric equivariant features for angiographic images. The experimental results demonstrate that we achieve a better performance compared with the state-of-the-art methods.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Zhang, Z. et al. (2024). Self-supervised Vessel Segmentation from X-ray Images using Digitally Reconstructed Radiographs. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_64
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DOI: https://doi.org/10.1007/978-3-658-44037-4_64
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