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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

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Ear Cartilage Inference for Reconstructive Surgery with Convolutional Mesh Autoencodershat requires the surgeon to carve a “scaffold” for a new ear, typically from the patient’s own rib cartilage. This is an unnecessarily invasive procedure, and reconstruction relies on the skill of the surgeon to accurately construct a scaffold that best suits the patient based on limited data. Work
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Robust Multi-modal 3D Patient Body Modelinglinical workflow, automated parameter optimization for medical devices .With the popularity of 3D optical sensors and the rise of deep learning, this problem has seen much recent development. However, existing art is mostly constrained by requiring specific types of sensors as well as limited data a
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A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Ob observed differential evolution for surgical navigation. Current endoscope tracking approaches still get trapped in image artifacts, tissue deformation, and inaccurate sensor outputs during endoscopic navigation. To deal with these limitations, we spatially constraint inaccurate electromagnetic sen
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Malocclusion Treatment Planning via PointNet Based Spatial Transformation Networkes on two key aspects: the treatment planning for dentition alignment; and the plan implementation with the aid of external forces. Existing treatment planning requires significant time and effort for orthodontists and technicians. At present, no work successfully automates the process of tooth move
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Reinforcement Learning of Musculoskeletal Control from Functional Simulationsscle activations for movements often being highly redundant, nonlinear, and time dependent, machine learning can provide a solution for their modeling and control for anatomy-specific musculoskeletal simulations. Sophisticated biomechanical simulations often require specialized computational environ
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