The invention relates to the technical field of medical
image processing, and discloses an adversarial generative
network model for three-dimensional reconstruction of a
brain cell-level
vascular network. The model comprises a three-dimensional
blood vessel feature extraction module, a dynamic attention generator, a multi-scale
discriminator and the like. Characteristics are obtained through multi-
modal medical image data (
magnetic resonance angiography and
confocal microscope scanning data), and high-precision
vascular network three-dimensional reconstruction is realized by utilizing
cooperative work of all the modules. The dynamic attention generator calculates the topological connection probability of the vascular branches, and the multi-scale
discriminator globally and locally evaluates the reconstruction result. Meanwhile, model training is optimized through an adversarial training controller, and the
blood vessel network is perfected through a
capillary network completion module. According to the method, the advantages of multi-
modal data are effectively fused, the reconstruction precision is improved, and powerful support is provided for research and diagnosis of brain vascular diseases.