TY - JOUR
T1 - Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging
T2 - New Reference Data
AU - Link, Daphna
AU - Braginsky, Michael B.
AU - Joskowicz, Leo
AU - Ben Sira, Liat
AU - Harel, Shaul
AU - Many, Ariel
AU - Tarrasch, Ricardo
AU - Malinger, Gustavo
AU - Artzi, Moran
AU - Kapoor, Cassandra
AU - Miller, Elka
AU - Ben Bashat, Dafna
N1 - Publisher Copyright:
© 2017 S. Karger AG, Basel.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
KW - Brain
KW - Brain segmentation
KW - Fetal brain development
KW - Fetal growth
KW - Fetal magnetic resonance imaging
KW - Intrauterine growth restriction
KW - Normal growth charts
UR - http://www.scopus.com/inward/record.url?scp=85029421990&partnerID=8YFLogxK
U2 - 10.1159/000475548
DO - 10.1159/000475548
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C2 - 28898865
AN - SCOPUS:85029421990
SN - 1015-3837
VL - 43
SP - 113
EP - 122
JO - Fetal Diagnosis and Therapy
JF - Fetal Diagnosis and Therapy
IS - 2
ER -