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Neurostereology Unbiased Stereology of Neural Systems von Mouton, P. R. (eBook)

  • Erscheinungsdatum: 12.11.2013
  • Verlag: Wiley-Blackwell
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Stereological methods provide researchers with unparalleled quantitative data from tissue samples and allow for well-evidenced research advances in a broad range of scientific fields. Presenting a concise introduction to the methodology and application of stereological research in neuroscience, Neurostereology provides a fuller understanding of the use of these methods in research and a means for replicating successful scientific approaches. Providing sound footing for future research, Neurostereology is a useful tool for basic and clinical researchers and advanced students looking to integrate these methods into their research.


    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 280
    Erscheinungsdatum: 12.11.2013
    Sprache: Englisch
    ISBN: 9781118444184
    Verlag: Wiley-Blackwell
    Größe: 14393 kBytes
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Stereological Estimation of Brain Volume and Surface Area from MR Images

Niyazi Acer1 and Mehmet Turgut2

1 Department of Anatomy, Erciyes University School of Medicine, Kayseri, Turkey

2 Department of Neurosurgery, Adnan Menderes University School of Medicine, Aydın, Turkey

Stereology combines mathematical and statistical approaches to estimate three-dimensional (3D) parameters of biological objects based on two-dimensional (2D) observations obtained from sections through arbitrary-shaped objects (for reviews of design-based stereology, see Howard and Reed, 1998; Mouton, 2002, 2011; Evans et al., 2004). Among the first-order parameters quantified using unbiased stereology are length using plane or sphere probes, surface area using lines, volume using points, and number using the 3D disector probe. These approaches estimate stereology parameters with known precision for any object regardless of its shape.

These criteria for stereological estimation of volume and surface area are met by standard magnetic resonance imaging (MRI) and computed tomography (CT) scans, as well as tissue sections separated by a known distance with systematic random sampling, that is, taking a random first section followed by systematic sampling through the entire reference space (Gundersen and Jensen, 1987; Regeur and Pakkenberg, 1989; Roberts et al., 2000; Mouton, 2002, 2011; García-Fiñ;ana et al., 2003; Acer et al., 2008, 2010). Numerous studies have been reported using MRI to estimate brain and related volumes by stereologic and segmentation methods in adults (Gur et al., 2002; Allen et al., 2003; Acer et al., 2007, 2008; Jovicich et al., 2009), children (Knickmeyer et al., 2008), and newborns (Anbeek et al., 2008; Weisenfeld and Warfield, 2009; Nisari et al., 2012).
The Cavalieri Principle

Named after the Italian mathematician Bonaventura Cavalieri (1598 - 1647), the Cavalieri principle estimates the first-order parameter volume (V) from an equidistant and parallel set of 2D slices through the 3D object. As detailed later, the approach uses the area on the cut surfaces of sections through the reference space (region of interest) to estimate size (volume) of whole organs and subregions of interest. The point counting technique for area estimation uses a point-grid system superimposed with random placement onto each section through the reference space (Gundersen and Jensen, 1987). The number of points falling within the reference area is counted for each section ( Figure 1.1 ). Total V of a 3D object, x , is estimated by Equation 1.1 :


where A(x) is the area of the section of the object passing through the point x ε ( a , b ), and b is the caliper diameter of the object perpendicular to section planes. The function A ( x ) is bounded and integratable in a bounded domain ( a , b ), which represents the orthogonal linear projection of the object on the sampling axis (García-Fiñ;ana et al., 2003; Kubínová et al., 2005).

Figure 1.1 Illustration of point counting grid overlaid on one brain section.

The Cavalieri estimator of volume is constructed from a sample of equidistant observations of f , with a distance T apart, as follows ( Eq. 1.2 ):


where x 0 is a uniform random variable in the interval (0, T ) and { f 1, f 2, ... , fn } is the set of equidistant observations of f at the sampling points which lie in ( a , b ). In many applications, Q represents the volume of a structure, and f ( x ) is the area of the intersection between the structure and a plane that is perpendicular to a given samplin

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