Focused on the issue that U-Net has a poor segmentation effect for small-volume pulmonary nodules, a segmentation method based on deep transfer learning was proposed, and Block Superimposed Fine-Tuning (BSFT) strategy was used to assist the segmentation of pulmonary nodules. Firstly, convolutional neural network was used to learn the feature information of large natural image datasets. Then, the learned features were transferred to the network for the segmentation of small pulmonary nodule image datasets. From the last sampling layer of the network, the network was released block by block and fine-tuned until the network completed the superimposition of the last layer. Finally, the similarity coefficient of Dice was quantitatively analyzed to determine the optimal segmentation network. The experimental results show that the Dice value of BSFT on LUNA16 pulmonary nodule open dataset reaches 0.917 9, which is obviously better than that of the mainstream pulmonary nodule segmentation algorithms.

Focusing on the issue that the significant localized characteristics of the influence of skull on the facial surface shape are not fully considered in the existing joint statistical craniofacial reconstruction methods based on Principal Component Analysis (PCA) modeling, which leads to the inadequate description ability of the craniofacial morphological correlation models, by employing these methods and describing the morphological relationship between skull and face, a new craniofacial reconstruction method based on a Partial Least Squares Regression (PLSR) model of local craniofacial morphological correlation was proposed. Firstly, the defects of the joint statistical shape model based on PCA with skull and face as a whole and the advantages of the local morphological correlation model based on PLSR were deeply analyzed. Secondly, by introducing PLSR into the modeling of craniofacial morphological correlation, and based on craniofacial 3D surface model, whose physiological consistent correspondence was established, and classified according to forensic anthropology knowledge, the PLSR coordinate calculation model for each vertex of facial surface was constructed, with those closely related vertex set on skull as its independent variables. Thirdly, with the coordinates of the unknown skull surface model as input values of the coordinate calculation model, the coordinate of each vertex of the predicted face model was acquired, from which the predicted face could be reconstructed, and the concrete procedure of the new reconstruction method was elaborated. Finally, several craniofacial reconstruction experimentations by applying the new reconstruction method based on PLSR were given, and the new reconstruction method was comparatively analyzed and evaluated by the indicators including effectiveness of reconstruction and absolute error. The experimental results show that the new reconstruction method significantly improves the accuracy of craniofacial reconstruction.