MRI-histology registration lays the ground for a new generation of high-resolution brain atlases. The task is challenging given the different contrast and the histology-related artifacts. We propose a dataset-specific, synthesis-based approach that uses a generative adversarial network to reduce the problem to intra-modality registration. Exploiting automatic segmentation data and cycle-consistency, the proposed architecture is suitable for small-size datasets. We show the advantages of this approach compared to canonical registration both in quantitative and qualitative terms using data from the Allen Institute’s Human Brain Atlas.