Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite
for many neuroimaging methods. Most existing methods exhibit disadvantages in
that they are laborious, yield inconsistent results, and/or require training data to closely
match the data to be processed. Here, we present pincram, an automatic, versatile method
for accurately labelling the adult brain on T1-weighted 3D MR head images. The method
uses an iterative refinement approach to propagate labels from multiple atlases to a given
target image using image registration. At each refinement level, a consensus label is generated.
At the subsequent level, the search for the brain boundary is constrained to the neighbourhood
of the boundary of this consensus label. The method achieves high accuracy
(Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of >
0.97) and performs better than many state-of-the-art methods as evidenced by independent
evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the
program generates the "success index," a scalar metadatum indicative of the accuracy of
the output label. Pincram is available as open source software.