The invention discloses a brain glioma segmentation method based on a cascaded convolutional neural network, and the method comprises the steps: carrying out the primary coarse segmentation of a braintumor region, and extracting the approximate position information of a tumor; expanding 10 pixels for each dimension on the basis of coarse segmentation and taking the 10 pixels as input of a fine segmentation network; improviing the fine segmentation network, so as to enable the fine segmentation network to combine the advantages of dense connection, an improved loss function and multi-dimensional model integration; designing an integrated model of three directions (2D, 2.5 D and 3DCNN models), and respectively considering all information of different resolutions corresponding to each direction; integrating post-processing operation condition random fields in a segmentation algorithm, and optimizing continuity of segmentation results in appearance and spatial positions. According to themethod, the brain glioma is segmented through the two-step cascaded convolutional neural network, the advantages of dense connection, a new loss function and multi-dimensional model integration are combined, an integration model in multiple directions is designed, and finally a segmentation result is optimized through a conditional random field.