Abstract
Non-contrast computed tomography (NCCT) is the primary first-line neuroimaging technique in the clinical workflow for patients with suspected ischemic stroke. We present a deep learning model to estimate the cerebral vessel tree from the NCCT instead of subsequently performed contrast-enhanced imaging techniques, e.g. computed tomography angiography (CTA). We employ a volumetric sliding window approach and feed the patches to a 3D U-Net. This U-Net has two outputs, a probability map that indicates vessel presence and a prediction of the corresponding CTA patch. The CTA regression target is used in addition to the supervised segmentation to optimize the 3D U-Net in a GAN-like manner in order to generate more realistic estimations for the vessel tree. Comparing our proposed model with the current state of the art for this task, a 2D U-Net operating on axial NCCT slices, we were able to slightly increase quantitative overlap metrics as well as achieve notably improved qualitative results w.r.t. spatial continuity of the segmented vessel tree.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Schauer, J., Thamm, F., Taubmann, O., Maier, A. (2023). Cerebral Vessel Tree Estimation from Non-contrast CT using Deep Learning Methods. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_15
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DOI: https://doi.org/10.1007/978-3-658-41657-7_15
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