Stroke is one of the common cerebrovascular diseases,which can be classified into acute(0-24 h),subacute(24 h-2 weeks)and chronic stage(>2 weeks).High-resolution T1-weighted imaging(T1WI)is one kind of magnetic resonance imaging(MRI)technique,which is main imaging mode of assessing the change of cerebral structure after stroke at chronic stage,and it can accurately judge the location and size of lesion.The precise segmentation for lesion is we accurately analyze dataset of neuroimaging in stroke with large scale,and is the key of predicting rehabilitation outcomes.The automatic segmentation algorithm is one kind of technique that can automatically segment data into many independent regions or object by specific regulation or model,which does not need manual intervention.Compared with manual segmentation,it has repeatable and scalable advantages,which does not need manual setting parameter.With the development of artificial intelligence technology,its application is gradually widespread in the field of medical imaging analysis.Adopting automatic segmentation algorithm to conduct reliable and repeatable segmentation for the lesions in T1WI,which can effectively assist clinical diagnosis and rehabilitation treatment for stroke.This review summarized the advance research of automatic segmentation algorithm based on T1WI for the lesion of chronic stroke from two aspects included conventional machine learning and deep learning,and the optimization approach of three kinds of algorithm structures,which can provide reference and inspiration for researching small lesion with higher difficulty in the field of automatic segmentation.

Stroke is one of the common cerebrovascular diseases,which can be classified into acute(0-24 h),subacute(24 h-2 weeks)and chronic stage(>2 weeks).High-resolution T1-weighted imaging(T1WI)is one kind of magnetic resonance imaging(MRI)technique,which is main imaging mode of assessing the change of cerebral structure after stroke at chronic stage,and it can accurately judge the location and size of lesion.The precise segmentation for lesion is we accurately analyze dataset of neuroimaging in stroke with large scale,and is the key of predicting rehabilitation outcomes.The automatic segmentation algorithm is one kind of technique that can automatically segment data into many independent regions or object by specific regulation or model,which does not need manual intervention.Compared with manual segmentation,it has repeatable and scalable advantages,which does not need manual setting parameter.With the development of artificial intelligence technology,its application is gradually widespread in the field of medical imaging analysis.Adopting automatic segmentation algorithm to conduct reliable and repeatable segmentation for the lesions in T1WI,which can effectively assist clinical diagnosis and rehabilitation treatment for stroke.This review summarized the advance research of automatic segmentation algorithm based on T1WI for the lesion of chronic stroke from two aspects included conventional machine learning and deep learning,and the optimization approach of three kinds of algorithm structures,which can provide reference and inspiration for researching small lesion with higher difficulty in the field of automatic segmentation.

Objective:To examine the survival status and explore factors related to death among human immunodeficiency virus （HIV） infected patients receiving antiretroviral therapy （ART） in Taizhou City during 2006‒2019. Methods:A retrospective cohort study was conducted to analyze the data on HIV-infected patients receiving ART in Taizhou during 2006‒2019. Kaplan-Meier （K-M） method was used to calculate the cumulative survival rate and cumulative treatment success rate. Cox regression model was used to determine survival status and factors associated with ART. Results:A total of 2 904 HIV-infected patients was included. The cumulative survival rate after 1， 3， 5， and 8 years of ART were 96.9%， 94.9%， 93.1% and 92.1%， respectively， and the cumulative treatment response rate were 91.3%， 85.3%， 81.8% and 73.8%， respectively. Compared with aged 18-30 years old， baseline CD4⁺T cell >350 count/μL， normal hemoglobin level， effective ART， no clinical symptom at baseline， and homosexual transmission， we found that aged 51-60 years old（HR=4.94，95%CI：1.66-14.69）， aged over 60 years old（HR=9.14，95%CI：3.14-26.63）， anemia at baseline（HR=2.24，95%CI：1.55-3.23）， baseline CD4⁺T cell <200 count/μL（HR=4.35，95%CI：2.14-8.86）， ART failure （HR=3.90，95%CI：2.73-5.58）， heterosexual transmission（HR=1.92，95%CI：1.15-3.20）， and signs of symptom at baseline（HR=1.68，95%CI：1.16-2.41） were risk factors of HIV-related death. Conclusion:The effect of ART in Taizhou City is confirmed with a high cumulative survival rate and treatment success. We should pay additional attention to senior HIV-infected patients with anemia at baseline and intensively monitor the effect of ART. Interventions such as “treat after discover” are supposed to be implemented more widely to further reduce HIV-related mortality.