Fetal MRI is also an essential tool for the study of normal as well as abnormal brain development in utero [Girard et al., 1995
; Prayer et al., 2006
; Perkins et al., 2008
; Rutherford et al., 2008
]. Although anatomical details can be usually visualized by prenatal ultrasound, layers of developing brain tissues do not display enough impedance difference to be delineated sonographically [Prayer et al., 2006
]. Fetal MRI is usually performed after 20 weeks gestational age (GA) when the main steps of organogenesis are completed. At this stage, the fetal brain consists of seven layers that may be visualized in vitro [Kostovic et al., 2002
]. On in vivo images, however, due to contrast limitations no more than four basic layers can be reliably identified. These include the cortical plate and subplate that together give rise to cortical grey matter (GM), the intermediate zone or fetal white matter (WM) and the ventricular zone also known as the germinal matrix (GMAT). The germinal matrix is a transient structure of developing cells adjacent to ventricles (VENT) that is present in the fetal brain between 8 and 28 weeks gestational age [Kinoshita et al., 2001
]. During embryology and early fetal life, the germinal matrix is a site of production of both neurons and glial cells which then migrate out to their final locations [Prayer et al., 2006
]. The volume of the germinal matrix reaches its peak at about 23–26 weeks GA and decreases subsequently [Battin et al., 1998
; Kinoshita et al., 2001
]. Due to its high cell-packing density, the germinal matrix appears hypointense on T2-weighted (T2w) MR images used in clinical practice, with intensities very similar to those of developing grey matter ().
A coronal view from an average MR T2w image of the young fetal brain. In addition to developing cortical grey matter (GM) and white matter (WM), a layer of the germinal matrix (GMAT) is located around ventricles (VENT).
Segmentation and quantitative analysis of main tissue types from clinical MR images of the fetal brain is essential for modeling of the normal brain development process and extracting rules to detect growth patterns that may be related to abnormal outcomes. Manual segmentation, however, is both tedious and time consuming for larger imaging studies. Automatic segmentation of the fetal brain, although challenging due to evolving states of tissue and how this is reflected on MR images, is necessary for clinical studies with multiple subjects.
Previous studies on automatic segmentation of developing human brain focused mainly on premature and term neonates [Huppi et al., 1998
; Inder et al., 2005
; Prastawa et al., 2005
; Xue et al., 2007
] and young children [Matsuzawa et al., 2001
; Murgasova et al., 2007
]. Among recent studies, Prastawa et al. 
developed an algorithm for automatic segmentation of the brain tissue from T1- and T2-weighted MR images of the newborn brain. The proposed three-step procedure included estimation of initial intensity distribution parameters using graph clustering, automatic bias correction and final refinement of segmentation with particular focus on identification of myelinated and non-myelinated white matter regions. Murgasova et al. 
presented an atlas-based approach for automatic segmentation of infant brain MRI where delineation of developing tissues is challenging due to ongoing process of white matter myelination. In another neonatal study, Xue et al. 
did not attempt to segment subcortical brain structures, but rather focused on precise automatic segmentation and reconstruction of the cortex from T2-weighted MR images. The proposed atlas-based method specifically targeted mislabeled partial volume voxels at the interface of grey matter and the cerebrospinal fluid. To address intensity variability in developing white matter, the global segmentation results were locally refined after splitting the brain volume into several regions.
Automatic analysis of MR images of the fetal brain has been so far restricted to processing of 2D slices. Claude et al. 
presented an approach to segmentation and biometric analysis of the posterior fossa from midline sagittal cross-sections. A semi-automatic method based on region growing was used to segment various components of the posterior fossa such as the brain stem or vermis and calculate biometric markers that may be indicative of fetal cerebellar growth. Grossman et al. 
performed quantitative measurements of the fetal brain from in utero MR images. As automatic segmentation was found inapplicable, cerebral, cerebellar and ventricular regions were traced manually on axial 2D MR slices. Approximate patterns of normal brain growth were then estimated through volumetric analysis of MR scans of 56 fetuses with gestational ages ranging from 25 to 41 weeks. Recently developed methods for reconstruction of motion-corrected 3D volumes from in utero MR scans [Rousseau et al., 2005; Rousseau et al., 2006
; Jiang et al., 2007
; Kim et al., in press
] have opened up the possibility of applying advanced image analysis methods to study the developing human brain in utero.
In this paper, we describe an approach to automatic segmentation of individual tissues from motion-corrected 3D MR images of the fetal brain. The method is aimed at extracting key brain regions, including developing grey and white matter as well as transient tissue types such as the germinal matrix. Due to substantial intensity overlap between various developing tissues (), some form of spatial context is necessary to achieve meaningful segmentation. For example, the almost complete intensity overlap between developing grey matter and the germinal matrix makes the interpretation of the latter mostly dependent on its location around ventricles. To address this issue and spatially constrain the segmentation process, we first create a probabilistic atlas of tissue distribution in the fetal brain from multiple manual delineations of reconstructed MR volumes. Then, we apply an atlas-based segmentation methodology to achieve feasible and anatomically correct segmentation of the developing tissues in new MRI scans. Quantitative validation indicates that automatic segmentation of the fetal brain produces reliable and reproducible results that may be used for further volumetric and morphometric analysis of the developing human brain in utero.
Figure 2 Distribution of voxel intensities in MR T2w image from modeled by fitted Gaussian probability density functions. Note large intensity variability and substantial intensity overlap between brain tissues, especially between developing grey matter (more ...)