  Accurate segmentation of fetal brain magnetic resonance images is crucial for
analyzing fetal brain development and detecting potential neurodevelopmental
abnormalities. Traditional deep learning-based automatic segmentation, although
effective, requires extensive training data with ground-truth labels, typically
produced by clinicians through a time-consuming annotation process. To overcome
this challenge, we propose a novel unsupervised segmentation method based on
multi-atlas segmentation, that accurately segments multiple tissues without
relying on labeled data for training. Our method employs a cascaded deep
learning network for 3D image registration, which computes small, incremental
deformations to the moving image to align it precisely with the fixed image.
This cascaded network can then be used to register multiple annotated images
with the image to be segmented, and combine the propagated labels to form a
refined segmentation. Our experiments demonstrate that the proposed cascaded
architecture outperforms the state-of-the-art registration methods that were
tested. Furthermore, the derived segmentation method achieves similar
performance and inference time to nnU-Net while only using a small subset of
annotated data for the multi-atlas segmentation task and none for training the
network. Our pipeline for registration and multi-atlas segmentation is publicly
available at https://github.com/ValBcn/CasReg.
