Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging

Frontiers in Neuroscience

Shizuko Akiyama¹  Jong-Min Lee²  Yangming Ou³  Seonggyu Kim⁴  Caitlin K. Rollins⁵  Cynthia M. Ortinau⁶  Judy A. Estroff⁷  Patricia Ellen Grant⁷  Hyuk Jin Yun⁸  Kiho Im⁸  Lana Vasung⁸  Jinwoo Hong⁹  Emiko Takeoka^1,10  Tomo Tarui^1,10  Gilsoon Park^1,11 

[1] 0Center for Perinatal and Neonatal Medicine, Tohoku University Hospital, Sendai, Japan;1Department of Biomedical Engineering, Hanyang University, Seoul, South Korea;Computational Health Informatics Program, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States;Department of Electronic Engineering, Hanyang University, Seoul, South Korea;Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States;Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, United States;Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States;Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States;Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States;Mother Infant Research Institute, Tufts Medical Center, Boston, MA, United States;USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States;

关键词: deep learning;    fetal MRI;    fetal brain;    brain age;    age prediction;   

DOI  :  10.3389/fnins.2021.714252

来源: DOAJ

【 摘 要 】

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.

【 授权许可】

Unknown