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.

翻译：胎儿脑磁共振图像的精确分割对于分析胎儿脑发育和检测潜在的神经发育异常至关重要。传统的基于深度学习的自动分割方法虽然有效，但需要大量带有真实标签的训练数据，而这些标签通常由临床医生通过耗时的标注过程生成。为克服这一挑战，我们提出了一种基于多图谱分割的新型无监督分割方法，能够在不依赖标注数据进行训练的情况下精确分割多种组织。该方法采用级联深度学习网络进行三维图像配准，该网络通过计算移动图像的小幅度增量形变，将其精确对齐至固定图像。该级联网络可用于将多个已标注图像配准至待分割图像，并通过融合传播的标签形成精细的分割结果。实验表明，所提出的级联架构性能优于测试的最新配准方法。此外，衍生分割方法在推理时间和分割性能上与nnU-Net相当，而仅需少量标注数据用于多图谱分割任务，且网络训练完全无需标注数据。我们的配准与多图谱分割流水线已开源发布至 https://github.com/ValBcn/CasReg。