Automated brain segmentation methods with a good precision and accuracy are required to detect subtle changes in brain volumes over time in clinical applications. However, the ability of established methods such as SIENA, US and kNN to estimate brain volume change have not been compared on the same data, nor been evaluated with ground-truth manual segmentations. We compared measurements of brain volume change between SIENA, US and kNN in terms of precision (repeatability) and accuracy (ground-truth) using one baseline and two repeated follow-up 1.5 T MRI scans after 4 years of 10 subjects. The coefficient of repeatability (brain volume/volume change) was larger for US (29.6 cc/2.84%) than for kNN (4.9 cc/0.31%) and SIENA (-/0.92%). In terms of absolute brain volume measurements US and kNN showed good correlation with the manual segmentations and with each other (all Spearman's correlation coefficients ρ≥0.96; all p<0.001). Concerning brain volume changes, SIENA showed a good (ρ=0.82; p=0.004), kNN a moderate (ρ=0.60; p=0.067) and US a weak (ρ=0.50; p=0.138) correlation with the manual segmentations. For measurements of volume change, SIENA-US (mean correlation coefficient and p-value: ρ=0.28; p=0.442) and US-kNN (ρ=0.17; p=0.641) showed a weak correlation, but correlation was fairly good for kNN-SIENA (ρ=0.65; p=0.048). In conclusion, US and kNN showed a good precision, accuracy and comparability for brain volume measurements. For measurements of volume change, SIENA showed the best performance. kNN is a good alternative if volume change measurements of other brain structures are required.