Semi-automatic segmentation of the fetal brain from magnetic resonance imaging

Jianan Wang; Emily S Nichols; Megan E Mueller; Barbra de Vrijer; Roy Eagleson; Charles A McKenzie; Sandrine de Ribaupierre; Emma G Duerden

doi:10.3389/fnins.2022.1027084

Semi-automatic segmentation of the fetal brain from magnetic resonance imaging

Front Neurosci. 2022 Nov 11:16:1027084. doi: 10.3389/fnins.2022.1027084. eCollection 2022.

Authors

Jianan Wang¹, Emily S Nichols^{2

3}, Megan E Mueller², Barbra de Vrijer⁴, Roy Eagleson^{1

3

5}, Charles A McKenzie⁶, Sandrine de Ribaupierre^{1

3

7

8}, Emma G Duerden^{1

2

3}

Affiliations

¹ Biomedical Engineering, Western University, London, ON, Canada.
² Applied Psychology, Faculty of Education, Western University, London, ON, Canada.
³ Western Institute for Neuroscience, Western University, London, ON, Canada.
⁴ Department of Obstetrics and Gynaecology, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
⁵ Department of Electrical and Computer Engineering, Western University, London, ON, Canada.
⁶ Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
⁷ Department of Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
⁸ Department of Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.

Abstract

Background: Volumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in magnetic resonance images (MRI) are often manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compared the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings.

Methods: Adult women with singleton pregnancies (n = 21), of whom five were scanned twice, approximately 3 weeks apart, were recruited [26 total datasets, median gestational age (GA) = 34.8, IQR = 30.9-36.6]. T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB's Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSCs). The DSC values were compared using Friedman's test for repeated measures.

Results: Comparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.72 [interquartile range (IQR) = 0.6-0.8] and 0.54 (IQR = 0.4-0.6), respectively. A Friedman's test indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT (p < 0.001).

Conclusion: Deformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development.

Keywords: MRI; brain; fetal; linear registration; nonlinear registration; volumetric reconstruction.