The automatic segmentation of the ipsilateral lung
U-Net-based
The pulmonary lobe region of the CT images was segmented by the previously trained in-house segmentation model first. The segmentation model was a 2.5D U-Net-based model that was constructed with a 2D encoder and a 3D decoder. The 2D encoder had the same structure as ResNet34, and the pre-trained weight on ImageNet was used to initialize the parameters. The model was trained using 1434 CT images with 58,019 slices and reached an average Dice of 92.8.After the region of the pulmonary lobes was segmented, the area of the ipsilateral lung was calculated and documented.