The embodiment of the invention provides a CT image
lung and trachea segmentation method and
system based on
deep learning, and the method employs a 2D UNet and 3D Unet
deep learning network model atthe same time, and comprises the following steps: S1, preprocessing; s2, carrying out two-dimensional
resampling; s3, carrying out two-dimensional segmentation; s4, performing three-dimensional sampling; s5, carrying out three-dimensional segmentation; and S6, fusion: carrying out combination operation on the two-dimensional and three-dimensional segmentation results of the
lung trachea to obtaina fused segmented
lung trachea, and then taking the maximum three-dimensional connected region in the calculation image as a final lung trachea segmentation result. According to the CT image lung trachea segmentation method and
system based on
deep learning, a 2D UNet network and a 3D Unet network are used at the same time, so that the trachea segmentation result is better, and the segmentation method is more robust. In the 3D UNet network training process, the training efficiency and precision of the network can be improved by a tracheal skeleton point sampling method.