This paper introduces a novel longitudinal image processing framework based on unbiased, robust, within-subject template creation for automatic surface reconstruction and segmentation of brain MRI across arbitrarily many time points. The framework addresses challenges in longitudinal image analysis, including processing bias, over-regularization, and limited processing to two time points. It demonstrates that treating all input images exactly the same is essential to reduce variability and avoid over-regularization by initializing processing with common information from the subject template. The results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations, making it valuable for clinical applications such as smaller sample sizes or shorter trials to establish disease-specific biomarkers or quantify drug effects. The framework is implemented in FreeSurfer and has been successfully applied in various studies, including Alzheimer's disease, Huntington's disease, and memory training. It has also been used for validating prospective motion correction. The methods are robust, reliable, and fully automated, allowing for unbiased longitudinal analysis. The approach involves generating an unbiased within-subject template by iteratively aligning all input images to a median image using a symmetric robust registration method. This template is then used to initialize segmentation and surface reconstruction procedures, reducing variability and improving accuracy. The framework also includes spatial normalization, intensity correction, Talairach registration, brainmask creation, normalization and atlas registration, subcortical segmentation, and surfaces reconstruction. These steps ensure that the longitudinal processing is unbiased and accurate, with the ability to detect subtle disease effects and reduce sample sizes. The methods have been validated on various datasets, including test-retest data and disease datasets, showing improved reliability and accuracy compared to traditional methods. The framework is designed to handle longitudinal data with multiple time points, providing a reliable and efficient solution for longitudinal image analysis.This paper introduces a novel longitudinal image processing framework based on unbiased, robust, within-subject template creation for automatic surface reconstruction and segmentation of brain MRI across arbitrarily many time points. The framework addresses challenges in longitudinal image analysis, including processing bias, over-regularization, and limited processing to two time points. It demonstrates that treating all input images exactly the same is essential to reduce variability and avoid over-regularization by initializing processing with common information from the subject template. The results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations, making it valuable for clinical applications such as smaller sample sizes or shorter trials to establish disease-specific biomarkers or quantify drug effects. The framework is implemented in FreeSurfer and has been successfully applied in various studies, including Alzheimer's disease, Huntington's disease, and memory training. It has also been used for validating prospective motion correction. The methods are robust, reliable, and fully automated, allowing for unbiased longitudinal analysis. The approach involves generating an unbiased within-subject template by iteratively aligning all input images to a median image using a symmetric robust registration method. This template is then used to initialize segmentation and surface reconstruction procedures, reducing variability and improving accuracy. The framework also includes spatial normalization, intensity correction, Talairach registration, brainmask creation, normalization and atlas registration, subcortical segmentation, and surfaces reconstruction. These steps ensure that the longitudinal processing is unbiased and accurate, with the ability to detect subtle disease effects and reduce sample sizes. The methods have been validated on various datasets, including test-retest data and disease datasets, showing improved reliability and accuracy compared to traditional methods. The framework is designed to handle longitudinal data with multiple time points, providing a reliable and efficient solution for longitudinal image analysis.