Improved V-Net-based lesion segmentation algorithm for intracranial hemorrhage
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 convolutionto 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.
deep learningV-Net modeldepth separable convolutionintracranial hemorrhage