Zusammenfassung
Fully automatic liver segmentation is important for the planning of liver interventions and decision support. In patients with HCC, dynamic-contrast enhanced MRI is particularly relevant. Previouswork has focused on liver segmentation in the late hepatobiliary contrast phase, which may not always be available in heterogeneous data from clinical routine. In this contribution, we demonstrate the training of a convolutional neural network across contrast phases of DCEMRI, that is on par with a specialized late-phase network (mean Dice score 0.96) but in addition is more robust to other contrast phase images compared with the specialized network.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Hänsch, A. et al. (2022). Robust Liver Segmentation with Deep Learning Across DCE-MRI Contrast Phases. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_3
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DOI: https://doi.org/10.1007/978-3-658-36932-3_3
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