The application discloses a double-
branch Raio gating graph aggregation multi-
modal meningioma segmentation method, belongs to the cross technical field of
computer vision and medical
image processing, is used for
meningioma segmentation, and comprises the following steps: preparing a
data set, constructing a deep neural
network model, and performing neural network training; inputting the
data set into the trained deep neural
network model; first inputting a double-
branch multi-
modal input module to output a multi-
modal fusion feature
tensor; then inputting the multi-modal fusion feature
tensor into a hierarchical Swin
Transformer encoder to output three-dimensional segmentation masks of enhanced tumors, tumor cores and whole tumor regions of
meningioma. Through double-
branch multi-modal input fusion, hierarchical Swin
Transformer coding, edge
perception modulation and Laplace gating graph aggregation, high-precision three-dimensional segmentation of enhanced tumors, tumor cores and whole tumor regions of meningioma is realized.