论文标题

使用胎儿组织注释数据集的自动多组织人胎儿脑分割基准

An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset

论文作者

Payette, Kelly, de Dumast, Priscille, Kebiri, Hamza, Ezhov, Ivan, Paetzold, Johannes C., Shit, Suprosanna, Iqbal, Asim, Khan, Romesa, Kottke, Raimund, Grehten, Patrice, Ji, Hui, Lanczi, Levente, Nagy, Marianna, Beresova, Monika, Nguyen, Thi Dao, Natalucci, Giancarlo, Karayannis, Theofanis, Menze, Bjoern, Cuadra, Meritxell Bach, Jakab, Andras

论文摘要

定量分析发育中的人类胎儿大脑以充分了解正常胎儿和先天性疾病患者的神经发育至关重要。为了促进这种分析,需要自动多组织胎儿脑分割算法,进而需要开放的分割胎儿大脑数据库。在这里,我们在7种不同的组织类别(外部脑脊液,灰质,白质,白质,心脏,灰色物质,深灰质物质,脑系统/脊柱)中,将50个手动分割的病理和非病理胎儿磁共振脑体积重建(20至33周)引入了公开可用的数据库。此外,我们定量评估了发展中胎儿大脑的几种自动多组织分割算法的准确性。四个研究小组参加了会议,总共提交了10种算法,证明了自动算法开发数据库的好处。

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 databases of segmented fetal brains. Here we introduce a publicly available database 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 database for the development of automatic algorithms.

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