Introduction. Development of the fetal brain surface with concomitant gyrification is one of the major maturational processes of the human brain. First delineated by postmortem studies or by ultrasound, MRI has recently become a powerful tool for studying in vivo the structural correlates of brain maturation. However, the quantitative measurement of fetal brain development is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution, the partial volume effect and the changing appearance of the developing brain. Today extensive efforts are made to deal with the “post-acquisition” reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution (Rousseau, F., 2006; Jiang, S., 2007). We here propose a framework devoted to the segmentation of the basal ganglia, the gray-white tissue segmentation, and in turn the 3D cortical reconstruction of the fetal brain. Method. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences in fetuses aged from 29 to 32 gestational weeks (slice thickness 5.4mm, in plane spatial resolution 1.09mm). For each fetus, 6 axial volumes shifted by 1 mm were acquired (about 1 min per volume). First, each volume is manually segmented to extract fetal brain from surrounding fetal and maternal tissues. Inhomogeneity intensity correction and linear intensity normalization are then performed. A high spatial resolution image of isotropic voxel size of 1.09 mm is created for each fetus as previously published by others (Rousseau, F., 2006). B-splines are used for the scattered data interpolation (Lee, 1997). Then, basal ganglia segmentation is performed on this super reconstructed volume using active contour framework with a Level Set implementation (Bach Cuadra, M., 2010). Once basal ganglia are removed from the image, brain tissue segmentation is performed (Bach Cuadra, M., 2009). The resulting white matter image is then binarized and further given as an input in the Freesurfer software (http://surfer.nmr.mgh.harvard.edu/) to provide accurate three-dimensional reconstructions of the fetal brain. Results. High-resolution images of the cerebral fetal brain, as obtained from the low-resolution acquired MRI, are presented for 4 subjects of age ranging from 29 to 32 GA. An example is depicted in Figure 1. Accuracy in the automated basal ganglia segmentation is compared with manual segmentation using measurement of Dice similarity (DSI), with values above 0.7 considering to be a very good agreement. In our sample we observed DSI values between 0.785 and 0.856. We further show the results of gray-white matter segmentation overlaid on the high-resolution gray-scale images. The results are visually checked for accuracy using the same principles as commonly accepted in adult neuroimaging. Preliminary 3D cortical reconstructions of the fetal brain are shown in Figure 2. Conclusion. We hereby present a complete pipeline for the automated extraction of accurate three-dimensional cortical surface of the fetal brain. These results are preliminary but promising, with the ultimate goal to provide “movie” of the normal gyral development. In turn, a precise knowledge of the normal fetal brain development will allow the quantification of subtle and early but clinically relevant deviations. Moreover, a precise understanding of the gyral development process may help to build hypotheses to understand the pathogenesis of several neurodevelopmental conditions in which gyrification have been shown to be altered (e.g. schizophrenia, autism…). References. Rousseau, F. (2006), 'Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain images', IEEE Transactions on Medical Imaging, vol. 13, no. 9, pp. 1072-1081. Jiang, S. (2007), 'MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies', IEEE Transactions on Medical Imaging, vol. 26, no. 7, pp. 967-980. Lee, S. (1997), 'Scattered data interpolation with multilevel B-splines', IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 3, pp. 228-244. Bach Cuadra, M. (2010), 'Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain', ISMRM Conference. Bach Cuadra, M. (2009), 'Brain tissue segmentation of fetal MR images', MICCAI.