Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

中文翻译：

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胎儿大脑 MRI 切片的联合大脑提取和图像质量评估框架

大脑提取和图像质量评估是胎儿脑磁共振成像 (MRI) 3D 重建和量化的两个基本步骤。然而，胎儿体位和朝向的随机性、胎儿大脑形态的多变性、胎儿周围母体器官的多变性以及数据样本的稀缺性，都增加了过多的噪声，给胎儿MRI切片的自动化大脑提取和质量评估带来了巨大的挑战。 。传统上，大脑提取和质量评估通常是独立进行的。然而，它们都专注于大脑图像表示，因此可以联合优化以确保网络学习更有效的特征并避免过度拟合。为此，我们提出了一种新颖的两阶段双任务深度学习框架，其中包括大脑定位阶段和双任务阶段，用于联合大脑提取和胎儿 MRI 切片的质量评估。具体来说，双任务模块紧凑地包含特征提取模块、质量评估头和具有特征融合的分割头，用于同时进行大脑提取和质量评估。此外，特征提取模块和分割头中引入了变压器架构。我们采用多步骤的训练策略来保证所有模块的稳定和成功的训练。最后，我们通过对不同质量的胎儿脑 MRI 切片的数据集进行 5 倍交叉验证和消融研究来验证我们的方法，并另外进行交叉数据集验证。实验表明，所提出的框架取得了非常有前途的性能。