Adaptive cuts for extracting specific white matter tracts

Extracting specific white matter tracts (e.g., uncinate fasciculus) from whole brain tractography has numerous applications in studying individual differences in white matter. Typically specific tracts are extracted manually, following replicable protocols which can be prohibitively expensive for large scale studies. A tract clustering framework is a suitable computational framework but from a neuroanatomical point of view, one of the key challenges is that it is very hard to design a universal similarity function for different types of white matter tracts (e.g., projection, association, commissural tracts). In this paper, we propose an adaptive cuts framework in which, using normalized cuts motivated objective function, we adaptively learn tract-tract similarity for each specific tract class using atlas based training data. Using the learnt similarity function we train an ensemble of binary support vector machines to extract specific tracts from unlabeled whole-brain tractography sets.