Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
This addresses the challenge of time-consuming manual annotation for clinicians in neurodevelopmental analysis, though it is incremental as it builds on existing multi-atlas and registration techniques.
The paper tackles the problem of segmenting fetal brain MRI without requiring extensive labeled training data by proposing an unsupervised method based on cascaded deep learning registration, achieving similar performance to nnU-Net while using only a small subset of annotated data and none for network training.
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.