Abstract
Brain tumors are the brain diseases with the highest mortality and prevalence, and magnetic resonance imaging has high-resolution and multiparameter. As the basis for realizing the quantitative analysis of brain tumors, automatic segmentation plays a vital role in diagnosis and treatment. A new network model is proposed to improve the accuracy of convolutional neural network segmentation of brain tumor regions and control the parameter space scale of the network model. The model first uses a convolutional layer composed of a series of 3D convolution filters to construct a backbone network for feature learning of input 3D MRI image blocks. Then, a pyramid structure constructed by a 3D convolutional layer is designed to extract and fuse features of tumor lesions and context information of different scales and then classify the fused feature at the voxel level to obtain segmentation results. Finally, a conditional random field is used to postprocess segmentation results for structured refinement. By designing massive ablation experiments to analyze the sensitivity of the essential modules of the comparison network, the results confirm that our method can better solve the problems faced by the traditional fully connected convolutional neural network.