Deep Learning is now well established as the most efficient method for medicalimage segmentation. Yet, it requires large training sets and ground-truth labels,annotated by clinicians in a time-consuming process. We propose an unsupervisedsegmentation method using multi-atlas registration. The architecture of our regis-tration model is composed of cascaded networks that produce small amounts ofdisplacement to warp progressively the moving image towards the fixed image.Once the networks are trained, multiple annotated magnetic resonance (MR) fetalbrain images and their labels are registered with the image to segment, the resultingwarped labels are then combined to form a refined segmentation. Experiments showthat our cascaded architecture outperforms the state-of-the-art registration methodsby a significant margin. Furthermore, the derived segmentation method obtainssimilar results as one of the most robust state-of-the-art segmentation methods,without using any labels during training.