Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
Sci Data. 2021 Jul 6;8(1):167. doi: 10.1038/s41597-021-00946-3.
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.
为了充分了解正常胎儿和先天性疾病胎儿的神经发育情况，定量分析发育中的人类胎儿大脑至关重要。为了促进这种分析，需要自动的多组织胎儿大脑分割算法，而这反过来又需要分割的胎儿大脑的开放数据集。在这里，我们引入了一个公开的数据集，其中包含 50 个手动分割的病理性和非病理性胎儿磁共振脑体积重建，涵盖了一系列胎龄（20 至 33 周），分为 7 个不同的组织类别（外部脑脊液、灰质、白质、脑室、小脑、深部灰质、脑干/脊髓）。此外，我们还定量评估了几种用于发育中的人类胎儿大脑的自动多组织分割算法的准确性。四个研究小组参与了评估，共提交了 10 种算法，这表明该数据集对于自动算法的开发非常有益。
