Research on Lung Nodule Segmentation Algorithm Based on Improved V-Net Model
Since CT images are three-dimensional images,in the original V-Net model segmentation,it is prone to the problems of nodal omission and unclear boundary segmentation,as well as the instability during the training of the loss function Dice.According to these problems,we propose 3D multi-scale SE V-Net referred to as MSEV-Net network,while improving the stability of training by joint loss function.This network model is based on the V-Net network,using the multi-scale convolution module to replace the original 5×5×5 convolution,while adding the SE channel attention module after the residual connection to solve the problem of small lung nodules that are not easy to segment by fusing features of different scales and learning the relationship between different channels.At the same time,a short jump connection is added on top of the residual connection of the V-Net network,which makes the whole network better utilize the global features.The joint loss function selects Dice and cross-entropy loss function for fusion,which can well solve the problem of training instability.The MSEV-Net network model and joint loss function proposed reach 0.998 in the average segmentation accuracy PA and 0.837 in the DSC.The experimental results show that the proposed method is effective in improving the segmentation accuracy of lung nodules.
pulmonary nodule segmentationV-Net networkjoint loss functionmulti-scale convolutionSE modules
万方数据
维普
