Abstract：
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team\'s algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
摘要：
子宫内胎儿MRI正在成为诊断和分析发育中的人脑的重要工具。在研究和临床背景下，发育中的胎儿大脑的自动分割是产前神经发育定量分析的重要步骤。然而,大脑结构的手动分割是耗时的，容易出错和观察者之间的差异。因此,我们在2021年组织了胎儿组织注释（FeTA）挑战赛，以鼓励在国际上开发自动分割算法。挑战利用了FeTA数据集，胎儿脑MRI重建的开放数据集分为七个不同的组织(外部脑脊液，灰质,白质,心室,小脑,脑干,深灰质）。20个国际团队参加了这次挑战，提交总共21种算法进行评估。在本文中,我们从技术和临床角度对结果进行了详细分析.所有参与者都依赖于深度学习方法，主要是U网，由于网络体系结构中存在一些可变性，优化，和图像预处理和后处理。大多数团队使用现有的医学成像深度学习框架。提交之间的主要区别是在培训期间进行的微调，以及执行的特定预处理和后处理步骤。挑战结果显示，几乎所有提交都表现相似。排名前五的团队中有四个使用了合奏学习方法。然而,一个团队的算法表现明显优于其他提交，由非对称U-Net网络体系结构组成。本文为子宫内发育中的人脑的未来自动多组织分割算法提供了第一个基准。
