基于多模态深度学习的脑肿瘤分割实验研究

In view of the existing problems of 3D U-Net network in brain tumor segmentation, such as difficult to reduce the value of loss function in the training process, poor segmentation accuracy of enhanced tumor and tumor core, an optimization scheme for the model network is proposed in this paper. First, the residual network structure is used to decrease the difficulty of training. Furthermore, the attention mechanism is adopted for fusion weights adaptive learning of multimodal MRI to make full use of different modal characteristic information. Finally, the two-path convolution structure is used in the network decoder part to improve the capability of feature extraction of the network. The experimental results show that the training loss function of the improved network is easier to converge to a smaller value, the average segmentation Dice coefficient of the three kinds of tumors is increased by 0.018 9, and the average Hausdorff distance is shortened by 1.197 1, which is better than the network before improvement in the overall segmentation performance.