Segmentation of fetal brain tissue from magnetic resonance imaging (MRI)
plays a crucial role in the study of in utero neurodevelopment. However,
automated tools face substantial domain shift challenges as they must be robust
to highly heterogeneous clinical data, often limited in numbers and lacking
annotations. Indeed, high variability of the fetal brain morphology, MRI
acquisition parameters, and superresolution reconstruction (SR) algorithms
adversely affect the model's performance when evaluated out-of-domain. In this
work, we introduce FetalSynthSeg, a domain randomization method to segment
fetal brain MRI, inspired by SynthSeg. Our results show that models trained
solely on synthetic data outperform models trained on real data in out-ofdomain
settings, validated on a 120-subject cross-domain dataset. Furthermore, we
extend our evaluation to 40 subjects acquired using lowfield (0.55T) MRI and
reconstructed with novel SR models, showcasing robustness across different
magnetic field strengths and SR algorithms. Leveraging a generative synthetic
approach, we tackle the domain shift problem in fetal brain MRI and offer
compelling prospects for applications in fields with limited and highly
heterogeneous data.

FetalSynthSeg 是一种基于 SynthSeg 的领域随机化方法，用于通过磁共振成像（MRI）对胎儿脑进行分割，研究结果表明，在跨领域数据验证中，仅使用合成数据训练的模型表现优于仅使用真实数据训练的模型，并且在不同磁场强度和超分辨率算法下具有鲁棒性。

利用合成数据改进胎儿 MRI 中的跨领域脑组织分割

Improving cross-domain brain tissue segmentation in fetal MRI with  synthetic data

Automatic fetal brain tissue segmentation can enhance the quantitative
assessment of brain development at this critical stage. Deep learning methods
represent the state of the art in medical image segmentation and have also
achieved impressive results in brain segmentation. However, effective training
of a deep learning model to perform this task requires a large number of
training images to represent the rapid development of the transient fetal brain
structures. On the other hand, manual multi-label segmentation of a large
number of 3D images is prohibitive. To address this challenge, we segmented 272
training images, covering 19-39 gestational weeks, using an automatic
multi-atlas segmentation strategy based on deformable registration and
probabilistic atlas fusion, and manually corrected large errors in those
segmentations. Since this process generated a large training dataset with noisy
segmentations, we developed a novel label smoothing procedure and a loss
function to train a deep learning model with smoothed noisy segmentations. Our
proposed methods properly account for the uncertainty in tissue boundaries. We
evaluated our method on 23 manually-segmented test images of a separate set of
fetuses. Results show that our method achieves an average Dice similarity
coefficient of 0.893 and 0.916 for the transient structures of younger and
older fetuses, respectively. Our method generated results that were
significantly more accurate than several state-of-the-art methods including
nnU-Net that achieved the closest results to our method. Our trained model can
serve as a valuable tool to enhance the accuracy and reproducibility of fetal
brain analysis in MRI.

本论文提出了一种使用深度学习训练的多标记胎儿脑部图像分割模型，利用自动选图和手动修复结合的多重配准策略来处理不可比较的胎儿脑部结构，采用标签平滑方法进行多噪声训练，使输出更准确，经测试后得到了更好的分割结果，有望成为胎儿 MRI 分析中提高准确度和可重复性的重要工具。