A new method for data-driven multi-brain atlas generation

We introduce a method for the automatic generation of magnetic resonance imaging brain atlases from a set of brains without any prior on their structure and number. This is a bottom-up, data-driven approach whose goal is to generate brain atlases that mirror as closely as possible the properties of arbitrary brains. This is important, for example, in the segmentation of brain structures of highly deformed brains as that of advanced Alzheimer´s disease patients. The method is as follows. First, we register all brain pairs and obtain similarity weights. Second, we construct a graph whose nodes represent volumes and the links the similarity weights. Third, we use Dijkstra´s method to select atlas candidates. Fourth, we iteratively merge clusters of atlas candidates until a good subset of them remains. We applied our method to a set of 61 ADNI brain volumes and found that a good selection could be made.