Brain tumor MRI image segmentation network based on multi-scale feature information
To address the issue of low segmentation accuracy in brain tumor magnetic resonance imaging(MRI)due to over-lapping brain tissue boundaries and image noise interference,a brain tumor segmentation model based on multi-scale feature informa-tion is proposed.This model incorporates the latest technology such as the attention mechanism into a 2D U-Net network.It utilizes a unique information fusion approach and a dual-branch module composed of Transformer and convolutional neural network parallel structures to extract multi-scale information features from both global and local regions,thereby highlighting the pathological infor-mation in tumor areas.The model was evaluated based on the standard Figshare brain tumor dataset.The experimental results show improvements in Dice score,average Jaccard index,Precision,and Recall by 3.01%,2.6%,3.08%and 4.73%,respectively,while the HD95 metric decreased by 0.1187.These evaluation metrics outperform existing state-of-the-art methods.Additionally,ablation experiments demonstrate that both the information fusion module and the dual-branch module contribute to enhancing the accuracy of existing brain tumor MRI segmentation.
Brain tumor segmentationAttention mechanismParallel structureMulti-scale information
NETL
NSTL
万方数据
维普
