Lightweight Brain Tumor Segmentation Algorithm Based on Multi-View Convolution

Brain tumor segmentation is significant in the development of medical image processing and human health. Owing to the high complexity and sophisticated requirements for the hardware equipment of 3D convolutional neural networks, this paper proposes a lightweight multi-view convolutional brain tumor segmentation algorithm. First, a multiplexer module is used to effectively fuse the information between each channel, and the extraction ability for nonlinear features is added to the model. Second, pseudo 3D convolution is used to perform convolution from axial, sagittal, and coronal positions, and group convolution is added to save computing resources and reduce device memory usage. Finally, trainable parameters are used to weigh the importance of features extracted from different views to improve the segmentation accuracy of the model. In addition, the distributed data parallel method is used to train the model to improve the graphics processing unit (GPU) usage. Experiments on the public dataset of the 2019 Brain Tumor Segmentation competition demonstrate that the average Dice similarity coefficient of the proposed algorithm is only 2. 52 percentage points lower than that of the first-place algorithm, however, number of parameters and floating-point operations are reduced by 84. 83% and 96. 67% respectively, and the average Dice similarity coefficient is 0. 05% higher than that of the runner-up algorithm. A comparative experimental analysis verifies the accuracy and lightness of the proposed algorithm, indicating the possibility for wide applicability of brain tumor segmentation models.

Automated summary

This paper proposes a lightweight multi-view convolutional brain tumor segmentation algorithm. It effectively fuses information between channels, adds extraction ability for nonlinear features, performs convolution from different positions, and uses group convolution to save resources. It also weighs the importance of features extracted from different views. Experimental results show that the proposed algorithm achieves high segmentation accuracy with reduced parameters and floating-point operations.