A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual UNet Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT Images by Dual UNet algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual UNet is trained according the different lung regions. The first UNet algorithm operates these middle chest images and other images are operated by the second UNet algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single UNet and UNet algorithm.

DBT Masses Automatic Segmentation Using UNet Neural Networks To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a UNet architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the tophat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient UNet neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the UNet model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed UNetbased DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.