Segmentation of Pulmonary Nodules Based on BBClstm Unet

Abstract

To solve the problem of insufficient segmentation accuracy caused by the failure of UNET model to make full use of the weight relationship and semantic information between coding layers in lung nodule image segmentation, a lung nodule segmentation method based on BBClstm-Unet is proposed. In this method, parallel attention module is used to complete the information coding of a larger range of pulmonary nodules image and reset the weight of sampling correlation channel information. Bconvlstm structure is used to combine feature mapping, and forward and backward output sampling are fused to obtain the final segmentation result. At the same time, hybrid loss function is used to alleviate the class imbalance problem. The experimental results show that the average dice value of BBClstm-Unet network on luna16 data set reaches 90.83%, which is better than the original UNET network.