【摘要】 针对颅内出血病灶分割不精确问题提出一种改进V-Net算法。用深度可分离卷积去替换普通卷积，加快模型训练速度。在编码器和解码器中分别加入通道注意力机制和混合注意力机制。通过引入SE模块和CBAM模块，强化原始网络的特征提取能力以及自适应调整特征图中不同通道之间的权重，提高模型的性能表现。对比实验结果表明，改进后的V-Net分割评价指标DSC达到0.732,比原始V-Net提升4.4%。更多还原

【Abstract】 An improved V-Net algorithm is proposed to address the inaccurate segmentation of intracranial hemorrhage lesions. The depth-separable convolution is used to replace the normal convolution to speed up the model training. A channel attention mechanism and a hybrid attention mechanism are added to the encoder and decoder, respectively. By introducing the SE module and CBAM module, the feature extraction capability of the original network is enhanced as well as the adaptive adjustment of the weights between different channels in the feature map to improve the performance of the model. The comparison experimental results show that the improved V-Net segmentation evaluation index DSC reaches 0.732, which is 4.4% better than the original V-Net.更多还原