TY - JOUR AB - Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisitefor many neuroimaging methods. Most existing methods exhibit disadvantages inthat they are laborious, yield inconsistent results, and/or require training data to closelymatch the data to be processed. Here, we present pincram, an automatic, versatile methodfor accurately labelling the adult brain on T1-weighted 3D MR head images. The methoduses an iterative refinement approach to propagate labels from multiple atlases to a giventarget 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 neighbourhoodof 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 independentevaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, theprogram generates the "success index," a scalar metadatum indicative of the accuracy ofthe output label. Pincram is available as open source software. AU - Heckemann,RA AU - Ledig,C AU - Gray,KR AU - Aljabar,P AU - Rueckert,D AU - Hajnal,JV AU - Hammers,A DO - 10.1371/journal.pone.0129211 PY - 2015/// SN - 1932-6203 TI - Brain Extraction Using Label Propagation and Group Agreement: Pincram T2 - PLOS One UR - http://dx.doi.org/10.1371/journal.pone.0129211 UR - http://hdl.handle.net/10044/1/30750 VL - 10 ER -