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Thickness and design features of clinical cranial implants - what should automated methods strive to replicate?

, , , , , and . Int. J. Comput. Assist. Radiol. Surg., 19 (4): 747-756 (April 2024)

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A U-Net Based System for Cranial Implant Design with Pre-processing and Learned Implant Filtering., , , , , , and . AutoImplant@MICCAI, volume 13123 of Lecture Notes in Computer Science, page 63-79. Springer, (2021)Thickness and design features of clinical cranial implants - what should automated methods strive to replicate?, , , , , and . Int. J. Comput. Assist. Radiol. Surg., 19 (4): 747-756 (April 2024)Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge., , and . AutoImplant@MICCAI, volume 12439 of Lecture Notes in Computer Science, page 65-76. Springer, (2020)Comparing densely calculated facial soft tissue depths for craniofacial reconstruction: Euclidean vs. perpendicular distances., , and . Comput. methods Biomech. Biomed. Eng. Imaging Vis., (January 2024)AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design., , , , , , , , , and 29 other author(s). IEEE Trans. Medical Imaging, 40 (9): 2329-2342 (2021)Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge., , , , , , , , , and 27 other author(s). Medical Image Anal., (August 2023)Validating 3D face morphing towards improving pre-operative planning in facial reconstruction surgery., , , , and . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 9 (5): 480-487 (2021)