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Found 4 papers, 3 papers with code
Introducing Learning Rate Adaptation CMA-ES into Rigid 2D/3D Registration for Robotic Navigation in Spine Surgery
The covariance matrix adaptive evolution strategy (CMA-ES) has been widely used in the field of 2D/3D registration in recent years.
An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES
While artificial intelligence technology has advanced rapidly in recent years, traditional optimization-based registration methods remain indispensable in the field of 2D/3D registration. he exceptional precision of this method enables it to be considered as a post-processing step of the learning-based methods, thereby offering a reliable assurance for registration.
Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion
Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions.
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard.
