Enabling machine learning in X-ray-based procedures via realistic simulation of image formation

Keyphrases

• Machine Learning 100%
• Convolutional Neural Network 100%
• X-ray 100%
• Image Formation 100%
• Ray-based 100%
• Realistic Simulation 100%
• Deep Learning 50%
• Nave 50%
• Fast Simulation 50%
• Fluoroscopy 50%
• Image Analysis 25%
• Publicly Available 25%
• Computation Time 25%
• Domain Adaptation 25%
• Material Decomposition 25%
• Machine Learning-based Approach 25%
• Cadaveric 25%
• Surgical Workflow 25%
• X-ray Images 25%
• X-ray System 25%
• Intraoperative Image 25%
• Complementary Approach 25%
• Anatomical Landmark Detection 25%
• Modeling Capability 25%
• Three-dimensional Computed Tomography 25%
• Forward Projection 25%
• Radiograph 25%
• Software Platform 25%
• Image-guided Procedures 25%
• Training Adaptation 25%
• Material Scatter 25%
• In Silico Modeling 25%
• Diagnostic Radiology 25%
• All Convolutional Neural Network 25%
• Python 25%
• High-resolution CT 25%
• Cadaveric Specimen 25%
• Image Style Transfer 25%
• Synthetic Testing 25%
• PyTorch 25%
• X-ray Image Analysis 25%
• Tool Model 25%
• Training Protocol 25%
• Procedural Guidance 25%
• Noise Injection 25%
• X-ray-guided 25%
• Scatter Estimation 25%
• Clinical Radiographs 25%
• Robot End-effector 25%

Neuroscience

• Neural Network 100%
• Fluoroscopy 40%

Computer Science

• Image Style Transfer 20%