Background: 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. Subjects and Methods: 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). Results: 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. Discussion: The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice.
Bibliographical noteFunding Information:
The authors are grateful to Vicki Myers for editorial assistance, to Liraz Olmer for assisting in curve creation, and to Faina Vitin-shtein, Tuvia Ganot, and MRI technicians at the Tel Aviv Sourasky Medical Center for help with patient recruitment and MRI scans. This work was supported by the Gulton Foundation.
© 2017 S. Karger AG, Basel.
- Brain segmentation
- Fetal brain development
- Fetal growth
- Fetal magnetic resonance imaging
- Intrauterine growth restriction
- Normal growth charts