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Mitral Valve Quantification at a Glance - Flattening Patient-Specific Valve Geometry., , , , , , , и . Bildverarbeitung für die Medizin, стр. 296-301. Springer Vieweg, (2019)Enrichment of cardiac pacemaker-like cells: neuregulin-1 and cyclic AMP increase I f-current density and connexin 40 mRNA levels in fetal cardiomyocytes., , , , , , , , и . Medical Biol. Eng. Comput., 45 (2): 221-227 (2007)Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries., , , , и . MICCAI (1), том 11070 из Lecture Notes in Computer Science, стр. 747-755. Springer, (2018)Abstract: Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training., , , , и . Bildverarbeitung für die Medizin, стр. 341. Springer, (2020)Accuracy evaluation of a mitral valve surgery assistance system based on optical tracking., , , , , , и . Int. J. Comput. Assist. Radiol. Surg., 11 (10): 1891-1904 (2016)Flexible and Comprehensive Patient-Specific Mitral Valve Silicone Models with Chordae Tendinae Made From 3D-Printable Molds., , , , , и . CoRR, (2019)Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements., , , , , , , , , и . Bildverarbeitung für die Medizin, стр. 160-165. Springer, (2022)Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks., , , , , , , и . Bildverarbeitung für die Medizin, стр. 22-27. Springer, (2021)Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training., , , , и . MICCAI (5), том 11768 из Lecture Notes in Computer Science, стр. 155-163. Springer, (2019)Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries., , , , и . Bildverarbeitung für die Medizin, стр. 282. Springer Vieweg, (2019)