Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers. In this paper, we propose to mitigate this issue with a hybrid approach for brain parcellation. We use diffusion MRI (dMRI) based structural connectivity measures to drive the refinement of an anatomical prior parcellation. Our method generates highly coherent structural parcels in native subject space while maintaining interpretability and correspondences across the population. This goal is achieved by registering a population-wide anatomical prior to individual dMRI scan and generating connectivity signatures for each voxel. The anatomical prior is then deformed by re-parcellating the brain according to the similarity between voxel connectivity signatures while constraining the number of parcels. We investigate a broad family of signature similarities known as AB-divergences and explain how a divergence adapted to our segmentation task can be selected. This divergence is used for parcellating a high-resolution dataset using two graph-based methods. The promising results obtained suggest that our approach produces coherent parcels and stronger connectomes than the original anatomical priors.