Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F₁-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.
Algorithms ; Retinal Vessels ; Image Processing, Computer-Assisted

Objective: To explore the improvement rate of liver fibrosis in patients with chronic hepatitis B virus infection who received entecavir alone or in combination with anluohuaxianwan for 78 weeks. Methods: Patients with chronic HBV infection were randomly treated with entecavir alone or in combination with anluohuaxian for 78 weeks. Ishak fibrosis score was used for blind interpretation of liver biopsy specimens. The improvement in liver fibrosis condition before and after the treatment was compared. Student's t test and non-parametric test (Mann-Whitney U-Test and Kruskal-Wallis test) were used to analyze the measurement data. The categorical variables were analyzed by Chi-square test method and Spearman’s rank correlation coefficient was used to test bivariate associations. Results: Liver fibrosis improvement rate after 78 weeks of treatment was 36.53% (80/219) and the progression rate was 23.29% (51/219). The improvement of liver fibrosis was associated to the degree of baseline fibrosis and treatment methods (P < 0.05). The improvement rate of hepatic fibrosis in patients treated with anluohuaxianwan combined with entecavir at baseline F < 3 (54.74%, 52/95) was significantly higher than that in patients treated only with entecavir (33.33%, 16/48), P = 0.016 and the progression rate of hepatic fibrosis (13.68%, 13/95) was lower than that in patients treated alone (18.75%, 9/48), P = 0.466. In patients with baseline F < 3, the proportion of patients with improved and stable liver fibrosis in the combined treatment group (68.1%, 32/47) was higher than that in the treatment group alone (51.7%, 15/29). Conclusion Combined anluohuaxianwan and entecavir treatment can significantly improve the improvement rate of liver fibrosis in patients with chronic hepatitis B virus infection. Furthermore, it has the tendency to improve the stability rate and reduce the rate of progression of liver fibrosis.