Sin clasificación en coyunturas desencadenantes en estudio.

2.7.1 Brain volumetry

Brain volumetry typically involves segmentation and quantification of various tissue types of the

brain such as GM, WM and CSF. Other Global metrics include total intracranial volume (TIV)

and total brain volume (TBV) which are volume estimates of the intracranial space and the brain

parenchyma respectively. These metrics are called global metrics as they are computed for the

brain as a whole which includes the brain cortex, subcortical regions, cerebellum, brainstem and

for TIV the fluid surrounding these structures. Regional brain volume metrics comprise of volumes

of anatomical brain structures like hippocampus, amygdala, thalamus, nucleus accumbens, etc

which constitute the subcortical structures. Other types of regional brain volumes measures include

lobes and regions that represent various functions like speech, vision, memory etc.

The volume of a brain tissue is measured by integrating the volumes of the voxels belonging

to the region of interest in a structural image. These regions of interest are segmented using manual

or automated methods. Manual brain segmentation is performed by individuals with training in

brain anatomy with the aid of MRI visualization software tools. This is a laborious procedure

which results in inter and intra-operator variability of the estimated volume. Nordenskjöld et al.,

(2013) reported that manual segmentation of TIV of a brain image with 50 image slices can take

up to 25 minutes for an experienced operator. Therefore, automated tools are increasingly used for

brain volumetry. A wide variety of automated methods are available for the segmentation of brain

images. A study by Helms, (2016) provides an extensive review of the available state-of-the-art

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The whole brain volume metrics are directly computed from the brain images by identifying

the voxels within the intensity range of the tissues. Whereas the regional brain volumes are

computed from the voxels delineated by identifying complex region boundaries that make these

regional structures. In practice, the voxels belonging to these complex brain structures are

identified by carefully registering standard brain atlases onto the brain image. Most of the work in

this thesis is focused on accurately estimating global brain volumes.

2.7.2 Automated Brain Image Segmentation

Brain segmentation involves accurately delineating the brain images into different tissues: GM,

WM, and CSF (Figure 2.9). A variety of automated segmentation methods currently exist of which

Statistical Parametric Mapping (SPM) (Ashburner and Friston, 2005), FreeSurfer

(surfer.nmr.mgh.harvard.edu/) (Dale et al., 1999; Fischl et al., 2002a), and FMRIB Software

Library (FSL) (https://fsl.fmrib.ox.ac.uk/fsl) (Smith et al., 2004) are the most widely used. This

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Figure 2.9 Brain tissues in a single section of a brain image from (A.) an MRI image, (B.)an average MRI brain tissue probabilistic map.

STATISTICAL PARAMETRIC MAPPING (SPM)

SPM uses an atlas-based Bayesian method to perform brain segmentation. SPM starts by

registering the subject’s brain image onto a tissue probability map (TPM). TPM contains prior probability maps of different tissue classes of the voxels in the standard space template. For linear

and non-linear registration SPM uses a default International Consortium for Brain Mapping space

template (ICBM; Rex et al., 2003) whose coordinates are defined in the Montreal Neurological

Institute (MNI) space. The unified segmentation algorithm also accounts for any biases present in

the image intensity. The segmentation algorithm performs tissue classification, bias correction and

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The above segmentation yields three types of images: (1) probabilistic tissue maps in the native

space of the subject, (2) probabilistic tissue intensity maps in the template space and (3) modulated

tissue intensity maps in the template space (Figure 2.10). The modulated images preserve the local

volumetric changes in grey and white matter intensity on a voxel-by-voxel basis while spatially

normalizing to the template. The modulated images are used for voxel-based morphometry (VBM)

(Ashburner and Friston, 2000) as they measure subtle changes in the anatomical brain structures

by removing the global affine effects (like scaling, rotation and translation). GM volume (GMV)

and WM volume (WMV) are obtained by integrating the product of the corresponding tissue

probabilities and the voxel volume in the native space of the subject. We will be using VBM to

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Figure 2.10 SPM brain segmentation pipeline illustrating probabilistic segmentation of GM, WM and CSF and voxel based morphometry (Ashburner and Friston, 2012).

FREESURFER

FreeSurfer utilizes a complex fully automated segmentation method for segmenting the brain into

various cortical and subcortical regions (Fischl et al., 2002a). Segmentation is done by modeling

a segment as an anisotropic Markov random field by using the priors for anatomical regions which

are obtained after registering the image onto a template. FreeSurfer uses recon-all pipeline for

segmentation, which also includes several preprocessing stages such as motion correction, non-

uniform intensity normalization, Talairach transform computation, intensity normalization, skull

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surface-based analysis of the brain. It creates a vertex map for surfaces which forms the interfaces

(e.g., WM-GM, GM-CSF) between different soft tissues.

FMRIB SOFTWARE LIBRARY (FSL)

FSL uses the SIENAX algorithm to perform brain extraction and tissue segmentation. SIENAX

first creates a brain mask for the T1 image using FSL’s brain extraction tool (BET) (Jenkinson et

al., 2002b). BET performs brain segmentation by growing a tessellated mesh inside the cranium

until the boundary between brain and CSF is reached. After brain extraction, brain tissues are

segmented using FSL’s Automated Segmentation Tool (FAST) (Zhang et al., 2001). FAST uses hidden Markov random field segmentation with expectation maximization algorithm and segments

the brain into GM and WM segmentations.