Longitudinal analysis of brain anatomical change can predict the growth or atrophy of human brain and provide a necessary foundation for clinical medicine application and research.However

due to different imaging machine or model and a long time interval of each image in different time point

the 3D image segmentation method can not provide adequate longitudinal stability of brain tissue variation.In this paper

we propose a 4D brain image level set segmentation model based on spatio-temporal information continuity.This model contain three terms:data term created by global and local information

spatial and temporal smooth term respectively.The data term reflects the intensity information of the image in each time point.The spatial and temporal term can keep the segmentation results smooth variation in these two dimensions.The experiments demonstrate that the proposed method can obtain a temporally consistent and spatially adaptive longitudinal brain image segmentation results.