The invention discloses an
unsupervised learning 2D and 3D
image registration method, and belongs to the field of computer medical
image processing. According to the method, a CNN-Transform parallel network and improved
projection space transformation based on a
parallel projection mode are constructed, specifically, 3D TOF and 2D DSA data are obtained and preprocessed, the improved
projection space transformation based on the
parallel projection mode is constructed, a parallel CNN-Transform double-
branch network (CTNet) is constructed, and the method is used for evaluating feature similarity, training the CTNet and achieving precise registration of the 2D DSA and the 3D TOF. The
system is used for interventional operation navigation, provides three-dimensional guidance for
catheter path planning and
embolism treatment, is used for complex
lesion assessment, and provides multi-aspect support for
disease diagnosis and treatment. According to the method, global and local features can be extracted more comprehensively for feature similarity evaluation, and the registration performance is improved; the
projection image is generated by adopting the improved
projection space transformation based on the
parallel projection mode, and the generated
projection image is more matched with the TOF imaging principle.