Lung computed tomography image segmentation based on U-Net network fused with dilated convolution

Abstract

PurposeIn order to solve the problem of accurate and effective segmentation of the patient's lung computed tomography (CT) images, so as to improve the efficiency of treating lung cancer. MethodWe propose a U-Net network (DC-U-Net) fused with dilated convolution, and compare the results of segmented lung CT with DC-U-Net, Otsu and region growth. We use Intersection over Union (IOU), Dice coefficient, Precision and Recall to evaluate the performance of the three algorithms. ResultsCompared with the common segmentation algorithm Otsu and region growing, the segmented image of DC-U-Net is closer to the Ground truth. The IOU of DC-U-Net is 0.9627, and the Dice coefficient is 0.9743, which is close to 1 and much higher than the other two algorithms. ConclusionWe propose that the model can directly segment the original image automatically, and the segmentation effect is good. This model speeds up the segmentation, simplifies the steps of medical image segmentation, and provides better segmentation for subsequent lung blood vessels, trachea and other tissues.