A brain tumor image segmentation method based on improved fully convolutional neural network

The segmentation accuracy of the existing machine learning algorithms for brain tumor image segmentation is not high. So a brain tumor image segmentation method based on improved fully convolutional neural network (FCNN) is proposed. Firstly, the MR brain tumor images of FLAIR T2 and T1C are gray normalized. And then gray image fusion method is used to get more brain tumor feature information. Furthermore, brain tumor image is segmented cursorily with the improved fully convolutional neural network which fusing the tertiary pooled feature information. In order to speed up the convergence degree and improve the accuracy of the training model, batch normalization (BN) layer is added after each convolutional layer. Finally, the conditional random fields (CRF) is integrated to the fully convolutional neural network to fine the segmentation result. Compared with the traditional MRI brain tumor segmentation methods of convolutional neural network (CNN), the experimental results show that the segmentation accuracy and stability has been greatly improved. Average Dice can be up to 91.29%. And the real-time performance of the proposed method is good. A brain tumor image can be segmented with the training model within an average of 1s.