Automatic quantification of normal cortical folding patterns from fetal brain MRI
Open accessAbstract
We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation-maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R(2)=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.
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- Authors
- Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M. A., Hajnal, J. V., Rueckert, D., & Aljabar, P.
- Affiliations
- Imperial College London (GB)
- St Thomas' Hospital (GB)
- Corresponding author
- Wright, R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 91
- Pages
- 21-32
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1987353801
- Cited by
- 134
- References
- 47
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 9 · 2023: 11 · 2022: 10 · 2021: 20 · 2020: 12 · 2019: 14 · 2018: 11 · 2017: 10 · 2016: 12 · 2015: 12 · 2014: 2
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- Funders
- National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- fetal brain MRI, gestational age estimation, Gompertz model, cortical parcellation, fetal brain development
- MeSH terms
- Adult, Algorithms, Atlases as Topic, Cerebral Cortex, Data Interpretation, Statistical, Female, Fetus, Gestational Age, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Pregnancy, Reproducibility of Results, Linear Models
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