Objective: To describe the status and trend of dietary energy, protein and fat pattern of Chinese residents. Method : 23 470 households including 68 962 subjects were randomly sampled from 132 counties in 31 provinces, autonomous regions and the municipalities. The 24 h dietary recall method for 3 consecutive days was used in dietary survey and weighing method was used for condiment intake. Results: The average energy intake per reference man per day was 9.42 MJ (2 250.5 kcal), 11.8% and 29.6% from protein and fat respectively. The percentages of energy from fat of big city residents arrived 38.4%. The percentages of energy from cereals, animal food and pure energy food were 57.9%, 12.6% and 17.3% respectively. The average protein intake per reference man per day was 65.9 g, 52%, 7.5% ,25.1% and 15.3% from cereals, bean and bean product, animal food and other foods respectively. The average fat intake per reference man per day was 76.3 g, 39.2% from animal food. Conclusion: The dietary change of Chinese residents represents the characteristics of the transition period, including the decrease of energy and protein intake per capita, decrease of the percentage of dietary energy supplied by cereals and increase of fat intake per capita,especially from animal foods, and the proportion of energy contributed by fat.

Objective Through clinical observation and statistics, to get the best curative effect of surgical operation in treating pigment nevus method and provide clinical guidance. Methods We reviewed of face and neck patients (1100 patients) with pigmented nevus in the department in the department of the dermatology,plastic surgery from January 2013 to October 2015, two different methods was designed on each parts and effect of the treatment, especially satisfaction degree was analyzed by statistical methods.Results In 100 cases of patients, only 20 patients had mild scar hyperplasia at the neck incision and the rest of the patients were satisfactory. For special parts such as mouth,nose and eye around,along thedirection of the muscle, arc and along the direction of dermatoglyph incision was designed respectively, patients obtained with higher postoperative satisfaction (P<0.05) . Conclusion In pigmented nevus of face and neck surgery treatment, surgical incision design requires dynamic and static combining method,incision design is important for the postoperative effect and patients' satisfaction.

Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves' ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.
Algorithms ; Image Processing, Computer-Assisted ; Neural Networks, Computer ; Tomography, X-Ray Computed

OBJECTIVE: To explore the feasibility of using the bidirectional local distance based medical similarity index (MSI) to evaluate automatic segmentation on medical images. METHODS: Taking the intermediate risk clinical target volume for nasopharyngeal carcinoma manually segmented by an experience radiation oncologist as region of interest, using Atlas-based and deep-learning-based methods to obtain automatic segmentation respectively, and calculated multiple MSI and Dice similarity coefficient (DSC) between manual segmentation and automatic segmentation. Then the difference between MSI and DSC was comparatively analyzed. RESULTS: DSC values for Atlas-based and deep-learning-based automatic segmentation were 0.73 and 0.84 respectively. MSI values for them varied between 0.29~0.78 and 0.44~0.91 under different inside-outside-level. CONCLUSIONS It is feasible to use MSI to evaluate the results of automatic segmentation. By setting the penalty coefficient, it can reflect phenomena such as under-delineation and over-delineation, and improve the sensitivity of medical image contour similarity evaluation.
Feasibility Studies ; Radiotherapy Planning, Computer-Assisted