Method for 2-D/3-D registration based on hierarchical pose regression

摘要

A method and apparatus for convolutional neural network (CNN) regression based 2D/3D registration of medical images is disclosed. A parameter space zone is determined based on transformation parameters corresponding to a digitally reconstructed radiograph (DRR) generated from the 3D medical image. Local image residual (LIR) features are calculated from local patches of the DRR and the X-ray image based on a set of 3D points in the 3D medical image extracted for the determined parameter space zone. Updated transformation parameters are calculated based on the LIR features using a hierarchical series of regressors trained for the determined parameter space zone. The hierarchical series of regressors includes a plurality of regressors each of which calculates updates for a respective subset of the transformation parameters.

主权项

1. A method for registering a 3D medical image with a 2D X-ray image, comprising:
determining a parameter space zone based on transformation parameters corresponding to a digitally reconstructed radiograph (DRR) generated from the 3D medical image; calculating local image residual (LIR) features from local patches of the DRR and the X-ray image based on a set of 3D points in the 3D medical image extracted for the determined parameter space zone; and calculating updated transformation parameters based on the LIR features using a hierarchical series of regressors trained for the determined parameter space zone, wherein the hierarchical series of regressors includes a plurality of regressors each of which calculates updates for a respective subset of the transformation parameters.