Structural T1-weighted magnetic resonance imaging (MRI) provides sufficient anatomical details to measure and track changes in volumes of brain structures. The volumes of brain structures and changes in them can be used to study the effects of disease, treatment monitoring, aging, learning and brain development. The present thesis investigates the requirements for performing reproducible quantitative brain volume measurements with automated brain tissue segmentation tools and gives an error bound on the measurements under various experimental conditions. A short introduction into the challenges of performing reproducible brain volume measurements and the main issues that impede the adoption of quantitative volumetric measurements in clinical practice is given, followed by an overview of the acquisition, reconstruction and automated image segmentation methods used to perform quantitative brain volume measurements. The first part of this study was carried out to investigate the reproducibility of volumetric measurements preformed on different systems with a standardized ADNI protocol. Systematic biases in volume measurements were observed when there were changes in systems between the first scan and rescan. An important finding in the context of patient management was that neither repositioning nor a two-week gap between the measurements did significantly contribute to the uncertainty in volumetric measurements when compared to the uncertainty in a back-to-back scan-rescan scenario. In the second part of this study, the impact of new highly-accelerated acquisition protocols on automated brain tissue volume measurements was investigated. A single system was used to collect the data and acquisition time was varied at the expense of the SNR. An important outcome of this study was that for qualitative assessment accelerated protocols provided similar information. However, the automated volume measurements with the highly-accelerated protocols were found biased compared to the measurements with standardized ADNI protocol. In the final part of this study, scaling procedures were investigated as means for compensating for the observed differences in sequential automated brain volume measurements. A new image-property-based compensation strategy was proposed and compared to the current state-of-the-art protocol-based approaches. The main outcomes of this study were that there are limitations to the current state-of-the-art protocol-based approaches, namely that volume correction coefficients used in the protocol-based approaches can vary as a function of age, and there is an indication that the proposed image-property-based approach can be more robust to the age and contrast-dependent effects compared to protocol-based approaches.