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4 April 2022 Statistical shape and pose modeling for automated planning in robot-assisted reduction of the ankle syndesmosis
Ali Uneri, Corey Simmerer, Wojciech Zbijewski, Runze Han, Gerhard Kleinszig, Sebastian Vogt, Kevin Cleary, Jeffrey H. Siewerdsen, Babar Shafiq
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Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341C (2022) https://doi.org/10.1117/12.2612958
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
Accurate, image-based planning of joint reduction based on intraoperative cone-beam CT forms the basis for precise robotic assistance and quantitative fluoroscopic guidance. The proposed approach combines statistical shape and pose modeling of the ankle joint to: (1) automatically segment individual bones; and (2) identify the target pose for the dislocated fibula to establish a plan for reduction. Leave-one-out analysis of the atlas members demonstrated accurate segmentation with 0.6 mm mean surface distance error and predicted the fibula pose within 1.6 mm and 1.8°. Future work will expand evaluation and analyze the appropriateness of the contralateral ankle as a patient-specific template.
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Ali Uneri, Corey Simmerer, Wojciech Zbijewski, Runze Han, Gerhard Kleinszig, Sebastian Vogt, Kevin Cleary, Jeffrey H. Siewerdsen, and Babar Shafiq "Statistical shape and pose modeling for automated planning in robot-assisted reduction of the ankle syndesmosis", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341C (4 April 2022); https://doi.org/10.1117/12.2612958
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KEYWORDS
Image segmentation
Statistical modeling
Bone
Error analysis
Robotics
Analytical research
Computed tomography
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