Fluorescence in Situ Hybridization Cell Image Segmentation Method.

Fluorescence in situ hybridization (FISH) is a molecular cytogenetic technique that provides reliable imaging biomarkers for the diagnosis of cancer and genetic diseases at the cellular level. An important prerequisite for identifying cancer cells in FISH images is the accurate segmentation of cells to quantify the DNA/RNA signal within each cell. However, FISH images often have problems such as low contrast, unclear cell boundaries, and potential cell defects, which hinder the complete identification and effective segmentation of cells. Therefore, this paper proposes a cell image segmentation method for fluorescence in situ hybridization. First, an improved adaptive histogram equalization is used to enhance the image. Then, the watershed segmentation method is used to calculate the optimal segmentation feature value, which is used as the seed point of the watershed method. The proposed method was applied to segment 273 cells from 5 images of leukemia cells, with a detection rate of 99% and an effectiveness of 94.5%. The results showed that the fluorescence in situ hybridization cell image segmentation method can accurately segment cells, and has a high detection rate and effectiveness.

Relationship between outpatients' sociodemographic and belief characteristics and their healthcare-seeking behavioral decision-making: Evidence from Jiaxing city, China.

BACKGROUND: China established the Tiered-network Healthcare Delivery System (THDS) in 2015 to address the disproportionate number of patients attending tertiary hospitals relative to primary- or secondary-care institutions. Although the reported number of outpatients visiting tertiary hospitals is slowly decreasing, numerous patients choose to visit them regardless of their disease's severity. To effectively implement the THDS, this article explored the relationship between patients' sociodemographic and belief characteristics and their healthcare-seeking behavioral decision-making in China. METHODS: Data obtained through questionnaires were analyzed using decision tree and logistic regression models to explore outpatients' characteristics and medical decision-making using comprehensive feature data. Moreover, further statistical analyses were conducted on the outpatient data obtained from the regional population health platform in Jiaxing, China. RESULTS: The decision tree model revealed that whether outpatients have medical insurance is the primary factor guiding their healthcare-seeking behaviors, with those without medical insurance more likely to choose primary or secondary hospitals to treat minor diseases. For those with medical insurance, profession is the main factor, with industrial workers more inclined to choose primary or secondary hospitals for minor diseases. The logistic regression analyses revealed that outpatients without insurance and who were not freelancers or individual owners were more likely to choose primary or secondary hospitals for minor diseases. Further statistical analysis of the data from the Jiaxing population health platform showed that, for minor or general diseases, outpatients without medical insurance and employed as farmers tended to choose primary and secondary hospitals over tertiary hospitals. CONCLUSION: The three analyses yielded consistent results: in China, medical insurance and patients' profession are the most important factors guiding outpatients' healthcare-seeking behaviors. Accordingly, we propose that the government should focus on economic reforms to increase outpatients' visits to primary and secondary hospitals and diagnosis-related groups' payment of medical insurance to decrease the admittance of patients with minor diseases in large tertiary hospitals. Meanwhile, the government should correct patients' belief prejudice about selecting hospitals through corresponding publicity.