Zusammenfassung
Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences.
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Engelhardt S, Sharan L, Karck M, et al. Cross-Domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training. In: Proc MICCAI 2019;. p. 155–163.
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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I. (2020). Abstract: Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_75
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DOI: https://doi.org/10.1007/978-3-658-29267-6_75
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