Deep learning-based medical image processing of lung CT

Aiming at the planning and navigation needs in lung disease surgery, and the time-consuming and laborious manual labeling of lung CT arterial segmentation, a 3DUNet architecture neural network incorporating CBAM attention mechanism is proposed for fast extraction of lung arterial veins. Based on the 2DUNet architecture, the dimension of the processed image is upgraded from two-dimensional to three-dimensional 3DUNet, and the CBAM attention module is added before pooling or up-convolution at the end of each convolution layer, and the parameter update is performed by using the Focal Loss loss function combined with stochastic gradient descent method. A three-fold cross-validation was performed using 95 lung CT data and ablation experiments were performed. After experimental validation, the model with the addition of CBAM attention module and Focal Loss loss function showed significant improvement in segmentation metrics, in which the accuracy, sensitivity, Dice coefficient, and precision for arteries and veins reached 99.8% and 99.8%, 84.9% and 87.2%, 82.5% and 84.8%, 81.1% and 83.5%, respectively.