Automatic quantification of normal cortical folding patterns from fetal brain MRI.
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.
Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK. Electronic address: r.wright11@imperial.ac.uk.
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R
Wright
Imaging Sciences & Biomedical Engineering Division, King's College London, St. Thomas' Hospital, SE1 7EH, UK.
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V
Kyriakopoulou
Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK.
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C
Ledig
Imaging Sciences & Biomedical Engineering Division, King's College London, St. Thomas' Hospital, SE1 7EH, UK.
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MA
Rutherford
Imaging Sciences & Biomedical Engineering Division, King's College London, St. Thomas' Hospital, SE1 7EH, UK.
J V
JV
Hajnal
Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK.
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D
Rueckert
Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK; Imaging Sciences & Biomedical Engineering Division, King's College London, St. Thomas' Hospital, SE1 7EH, UK.
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Aljabar
D000328:Adult; D000465:Algorithms; D001271:Atlases as Topic; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000196:embryology* / Q000254:growth & development; D003627:Data Interpretation, Statistical; D005260:Female; D005333:Fetus / Q000033:anatomy & histology*; D005865:Gestational Age; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011247:Pregnancy; D015203:Reproducibility of Results
Brain development; Cortical folding; Fetal MRI; Gompertz function