Link Daphna, Braginsky Michael B, Joskowicz Leo, Ben Sira Liat, Harel Shaul, Many Ariel, Tarrasch Ricardo, Malinger Gustavo, Artzi Moran, Kapoor Cassandra, Miller Elka, Ben Bashat Dafna
Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Fetal Diagn Ther. 2018;43(2):113-122. doi: 10.1159/000475548. Epub 2017 Sep 13.
Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort.
A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR).
The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart.
The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice.
准确估计胎儿脑容量对于评估胎儿发育至关重要。本研究的目的是开发一种从磁共振成像（MRI）数据中自动分割胎儿脑的方法，并首次基于大量队列创建正常体积生长图表。
开发了一种基于种子区域生长算法的半自动分割方法，并将其应用于199例孕18至37周发育正常胎儿的MRI数据。该算法的准确性与一组手动分割的真实数据进行了对比测试。采用二次回归分析创建正常生长图表。在9例宫内生长受限（IUGR）胎儿中验证了该方法识别发育障碍的敏感性。
所开发的方法与手动分割显示出高度相关性（r2 = 0.9183，p < 0.001），平均体积差异和体积重叠差异分别为4.77%和18.13%。创建了199例正常胎儿的新参考数据，所有9例IUGR胎儿均处于正常生长图表的第三百分位数或以下。
所提出的方法快速、准确、可重复、独立于用户、适用于回顾性数据，建议用于常规临床实践。
