The invention discloses a
brain glioma segmentation model and segmentation method based on
deep learning, belongs to the technical field of
brain glioma segmentation, and aims to solve the technical problem of how to accurately segment
brain glioma. The segmentation model comprises: a coding model which comprises a
convolution layer and N coding modules, wherein the coding model comprises a hole dense unit and a
pooling layer located at the output end of the hole dense unit; a decoding module, wherein the decoding module comprises a head end decoding module, a middle decoding module and a tailend decoding module which are connected in sequence, the output end of the head-end decoding module is in jump connection with the output end of the hole dense unit in the
tail-end encoding module through a jump connection layer, the output end of each intermediate decoding module is in jump connection with the output end of the hole dense unit in the corresponding intermediate coding module through a jump connection layer, the
tail end decoding module comprises a
convolution layer, a hole dense unit and a
convolution layer which are connected in sequence, and the
tail end decoding module islocated at the output end of the related jump layer of the previous decoding module.