Segmenting Left Atrial Appendage (LAA) from ultrasound image is an essential step for obtaining the clinical indicators, and the prerequisite and difficulty for automatic and accurate segmentation is locating the target accurately. Therefore, a method combining with automatic location based on deep learning and segmenting algorithm based on model was proposed to accomplish the automatic segmentation of LAA from ultrasound images. Firstly, You Only Look Once (YOLO) model was trained as the network structure for the automatic location of LAA. Secondly, the optimal weight files were determined by the validation set and the bounding box of LAA was predicted. Finally, based on the correct location, the bounding box was magnified 1.5 times as the initial contour, and C-V (Chan-Vese) model was utilized to realize the automatic segmentation of LAA. The performance of automatic segmentation was evaluated by 5 metrics, including accuracy, sensitivity, specificity, positive, and negative. The experimental results show that the proposed method can achieve a good automatic segmentation in different resolutions and visual modes, small samples data achieve the optimal location performance at 1000 iterations with a correct position rate of 72.25%, and C-V model can reach the accuracy of 98.09% based on the correct location. Therefore, deep learning is a rather promising technique in the automatic segmentation of LAA from ultrasound images, and it can provide a good initial contour for the segmentation algorithm based on contour.

Based on open source softwares of Computer Haptics, visualizAtion and Interactive in 3D (CHAI 3D) and Open Graphic Library (OpenGL), a virtual surgical system was designed for reduction of maxillary fracture. The virtual simulation scenario was constructed with real patients' CT data. A geomagic force feedback device was used to manipulate the virtual 3D models and output haptic feedback. On the basis of the original single finger-proxy algorithm, a multi-proxy collision algorithm was proposed to solve the problem that the tools might stab into the virtual organs during the simulation. In the virtual surgical system, the operator could use the force feedback device to choose, move and rotate the virtual skull model to simulate the movement and placement in real operation. The proposed system can be used to train medical students and for preoperative planning of complicated surgeries.

Labeling ordered XML documents can process XML data without accessing the data files. The present labeling schemes have achieved better results in queries, however, the labeling schemes for insertions incurs sacrifices of query performance, lower updates efficiency, and other problems. This paper proposed a new labeling scheme for insertions, EDL（Extended Dewey Labeling）, which efficiently realizes the calculations in the insertions of XML documents without degrading query performance . The conducted experiments have shown that EDL is superior to the similar labeling schemes for updates.

The fast increasing large and complex networks make the research of graph structure more and more important, in which approximate subgraph query is of big concern. Constructing index for each vertex by the adjacency characteristics was able to reduce the number of matched vertices, and partitioning the large graph based on label and structure information was able to reduce the matching search space. Using the dual index built in offline time, large amount of candidate vertices were filtered out according to the adjacency discriminant formula, and then the edge matching was carried out in some partition spaces. The experiments on real dataset show that, compared with many other existing methods, the dual-index query mechanism improves the efficiency and accuracy of subgraph matching significantly.