In vivo fetal magnetic resonance imaging provides a unique approach for the study of early human brain development [1]. In utero cerebral morphometry could potentially be used as a marker of the cerebral maturation and help to distinguish between normal and abnormal development in ambiguous situations. However, this quantitative approach is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution provided by very fast MRI sequences and the partial volume effect. Extensive efforts are made to deal with the reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution [2,3,4]. Frameworks were developed for the segmentation of specific regions of the fetal brain such as posterior fossa, brainstem or germinal matrix [5,6], or for the entire brain tissue [7,8], applying the Expectation-Maximization Markov Random Field (EM-MRF) framework. However, many of these previous works focused on the young fetus (i.e. before 24 weeks) and use anatomical atlas priors to segment the different tissue or regions. As most of the gyral development takes place after the 24th week, a comprehensive and clinically meaningful study of the fetal brain should not dismiss the third trimester of gestation. To cope with the rapidly changing appearance of the developing brain, some authors proposed a dynamic atlas [8]. To our opinion, this approach however faces a risk of circularity: each brain will be analyzed / deformed using the template of its biological age, potentially biasing the effective developmental delay. Here, we expand our previous work [9] to propose post-processing pipeline without prior that allow a comprehensive set of morphometric measurement devoted to clinical application. Data set & Methods: Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatial resolution 1.09mm). For each fetus, 6 axial volumes shifted by 1 mm were acquired under mother’s sedation (about 1min per volume). First, each volume is segmented semi-automatically using region-growing algorithms to extract fetal brain from surrounding maternal tissues. Inhomogeneity intensity correction [10] and linear intensity normalization are then performed. Brain tissues (CSF, GM and WM) are then segmented based on the low-resolution volumes as presented in [9]. A high-resolution image with isotropic voxel size of 1.09 mm is created as proposed in [2] and using B-splines for the scattered data interpolation [11]. Basal ganglia segmentation is performed using a levet set implementation on the high-resolution volume [12]. The resulting white matter image is then binarized and given as an input in FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to provide topologically accurate three-dimensional reconstructions of the fetal brain according to the local intensity gradient. References: [1] Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006. [3] Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio. Eng. 51(4) 2004. [6] Habas, MICCAI 2008. [7] Bertelsen, ISMRM 2009. [8] Habas, Neuroimage 53(2) 2010. [9] Bach Cuadra, IADB, MICCAI 2009. [10] Styner, IEEE TMI 19(39 (2000). [11] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997. [12] Bach Cuadra, ISMRM 2010.