改进UNet++的杉木CT图像年轮分割OA北大核心CSTPCD

In order to solve the problem that it is difficult to accurately segment tree rings with defects such as cracks,wormholes and knots.The medical CT is used as experimental equipment to reconstruct 125 CT images of Chinese fir transverse sections,and these images are used as the data set.Data set is expanded by pre-processing such as cutting,rotating and flipping CT images.An improved UNet++ model is proposed for tree rings segmentation.Convolutional blocks,downsampling layers,skip connections and upsampling layers have been added to the improved UNet++ model,and the learning depth is increased to 6 layers.The BCEWithLogitsLoss,ReLU and RMSProp are used as loss function,activa-tion function and optimization function respectively.The improved UNet++ model is used to segment the tree rings of the transverse sections of Chinese fir reconstructed by CT,and the performance of the model is evaluated.The results show that the pixel accuracy of the improved UNet++ model is 97.81% ,the dice coefficient is 98.89% ,the intersection over union is 95.29% ,and the mean intersection over union is 84.75% .The best segmentation result is obtained by fully extracting the characteristics in Chinese fir tree rings.Compared with the U-Net model and the UNet++ model,the improved UNet++ model makes the segmented tree rings complete and continuous,although most tree rings are cut by cracks and wormholes and cannot form a complete circular closed curve,fracture and noise are eliminated.The results show that the improved UNet++ model is not affected by defects such as cracks,knots and wormholes,and the segmentation results are very clear,which effectively solves mis-segmentation and under segmentation of dense tree rings under the interference of wormhole defects.