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Accuracy evaluation of a mitral valve surgery assistance system based on optical tracking.

, , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 11 (10): 1891-1904 (2016)

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Segmentation of the Left Atrium from 3D Gadolinium-Enhanced MR Images with Convolutional Neural Networks., , , and . STACOM@MICCAI, volume 11395 of Lecture Notes in Computer Science, page 265-272. Springer, (2018)Comprehensive patient-specific information preprocessing for cardiac surgery simulations., , , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 11 (6): 1051-1059 (2016)Interactive visual exploration of surgical process data., , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 18 (1): 127-137 (January 2023)An ex-vivo and in-vitro dynamic simulator for surgical and transcatheter mitral valve interventions., , , , , , , , , and 4 other author(s). Int. J. Comput. Assist. Radiol. Surg., 19 (3): 411-421 (March 2024)Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries., , , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 747-755. Springer, (2018)Surgical phase and instrument recognition: how to identify appropriate dataset splits., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 19 (4): 699-711 (April 2024)Mitral Valve Quantification at a Glance - Flattening Patient-Specific Valve Geometry., , , , , , , and . Bildverarbeitung für die Medizin, page 296-301. Springer Vieweg, (2019)How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?, , , , , , , , , and . CoRR, (2020)Flexible and comprehensive patient-specific mitral valve silicone models with chordae tendineae made from 3D-printable molds., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 14 (7): 1177-1186 (2019)Why is the winner the best?, , , , , , , , , and 41 other author(s). CoRR, (2023)