A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Lucas Fidon; Elizabeth Viola; Nada Mufti; Anna L David; Andrew Melbourne; Philippe Demaerel; Sébastien Ourselin; Tom Vercauteren; Jan Deprest; Michael Aertsen

doi:10.12688/openreseurope.13914.2

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Open Res Eur. 2022 Aug 31:1:123. doi: 10.12688/openreseurope.13914.2. eCollection 2021.

Authors

Lucas Fidon¹, Elizabeth Viola¹, Nada Mufti^{1

2}, Anna L David^{2

3}, Andrew Melbourne¹, Philippe Demaerel⁴, Sébastien Ourselin¹, Tom Vercauteren¹, Jan Deprest^{2

3

4}, Michael Aertsen⁴

Affiliations

¹ School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK.
² Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, WC1E 6DB, UK.
³ Department of Obstetrics and Gynaecology, University Hospitals Leuven, 3000 Leuven, Belgium.
⁴ Department of Radiology, University Hospitals Leuven, 3000 Leuven, Belgium.

Abstract

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA.

Keywords: Fetal brain atlas; anatomical landmarks; fetal brain T2-weighted MRI; fetal brain development; segmentation; spatio-temporal atlas; spina bifida aperta; super resolution and reconstruction.

Grants and funding

WT_/Wellcome Trust/United Kingdom