Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse...

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NeuroImage 27 (2005) 425 – 435

Automated segmentation of neuroanatomical structures in

multispectral MR microscopy of the mouse brain

Anjum A. Ali,a,* Anders M. Dale,b Alexandra Badea,a and G. Allan Johnsona

aCenter for In Vivo Microscopy, Box 3302, Duke University Medical Center, Durham, NC 27710, USAbDepartment of Neurosciences and Radiology, University of California, San Diego, 9500 Gilman Drive MC0662, La Jolla, CA 92093-0662, USA

Received 27 December 2004; revised 24 March 2005; accepted 5 April 2005

Available online 23 May 2005

We present the automated segmentation of magnetic resonance micro-

scopy (MRM) images of the C57BL/6J mouse brain into 21 neuro-

anatomical structures, including the ventricular system, corpus

callosum, hippocampus, caudate putamen, inferior colliculus, internal

capsule, globus pallidus, and substantia nigra. The segmentation

algorithm operates on multispectral, three-dimensional (3D) MR data

acquired at 90-Mm isotropic resolution. Probabilistic information used

in the segmentation is extracted from training datasets of T2-weighted,

proton density-weighted, and diffusion-weighted acquisitions. Spatial

information is employed in the form of prior probabilities of

occurrence of a structure at a location (location priors) and the

pairwise probabilities between structures (contextual priors). Valida-

tion using standard morphometry indices shows good consistency

between automatically segmented and manually traced data. Results

achieved in the mouse brain are comparable with those achieved in

human brain studies using similar techniques. The segmentation

algorithm shows excellent potential for routine morphological pheno-

typing of mouse models.

D 2005 Elsevier Inc. All rights reserved.

Keywords: Automated segmentation; Magnetic resonance microscopy;

Morphological phenotyping; Mouse brain

Introduction

Volumetric measurements, spatial organization of structures in

the brain, and 3D shape analysis are now recognized as critical

metrics of altered brain function and development (Caviness et al.,

1999). Human studies have shown deviation from normal structure

volume and shape in the presence of Alzheimer’s (Wang et al.,

2003), depression (Steffens et al., 2000), and schizophrenia (Gaser

et al., 2004). Magnetic resonance imaging (MRI) with its non-

invasive nature, exceptional soft tissue contrast, and 3D digital

format enables one to delineate structures and detect abnormalities

1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2005.04.017

* Corresponding author. Fax: +1 919 6847158.

E-mail address: [email protected] (A.A. Ali).

Available online on ScienceDirect (www.sciencedirect.com).

between normal and diseased brains. Suitable selection of imaging

parameters accentuates selected tissue properties based on proton

density (PD), T1, T2, T2*, and diffusion contrast. MRI thus lends

itself exceptionally well to morphometric studies in the brain where

clear distinction between structures is required.

The field of molecular genetics has produced animal models

that replicate disease found in humans. Conventional histological

sectioning of mouse models requires sectioning of specimens,

which can be time-consuming and labor-intensive. Magnetic

resonance microscopy (MRM), which is MRI with ultra-high

spatial resolution of 10–100 Am, provides us with an alternative

that is non-destructive and its 3D digital nature allows reslicing of

data in any plane. It also obviates the need for reconstruction and

alignment from sectioned slices. MRM can thus provide us with a

reliable measure of structure volume and shape in the study of

mouse models. We use the term magnetic resonance histology

(MRH) to describe the characterization of tissue structure (Johnson

et al., 1993). A normal adult mouse brain at 460 mg is

approximately 3000 times smaller than an adult human brain at

1400 g. One needs to scale the spatial resolution accordingly from

the 1-mm3 voxels used in the clinical arena to voxel volumes

<0.001 mm3. A number of studies have demonstrated the value of

3D MR morphometry in mouse models; the onset of atrophy in

brains of C57BL/6J and ApoE-deficient mice (McDaniel et al.,

2001); reduction in the volume of the anterior striatum of the

Dopamine transporter knock-out; and reduction in the dentate

gyrus before the onset of plaques in the PDAPP mouse (Redwine

et al., 2003).

A precursor to the morphometric measurement of brain

structures, however, is their segmentation. Segmentation in the

human brain using multispectral MR, initiated by Vannier et al.

(1985), has been used extensively (Andersen et al., 2002; Cline et

al., 1990; Fletcher et al., 1993; Lundervold and Storv, 1995; Pham

et al., 1997). Data collected from multiple channels aid in

differentiating between structure and tissue types that have

overlapping intensity histograms in one spectral space and

sufficient separation in another. Automated segmentation in the

human brain, until very recently, concentrated only on classifica-

tion of brain tissue into gray matter, white matter, and cerebrospi-

http://www.sciencedirect.com

Fig. 1. Large flip angle simulation for two tissues with different T1s (1100

ms and 900 ms) for 90- flip and 140- flip at different TRs.

A.A. Ali et al. / NeuroImage 27 (2005) 425–435426

nal fluid. Methods are either solely intensity-based (Gonzalez

Ballester et al., 2000; Kovacevic et al., 2002; Van Leemput et al.,

1999; Wells et al., 1996) or coupled with spatial constraints (Held

et al., 1997; Zhang et al., 2001). Increasing insights about

neurodegenerative disorders and brain development anomalies that

target a group of cells or a specific structure have focused research

on the sub-segmentation of gray matter and white matter into

physiologically relevant constituent structures. Individual struc-

tures are characteristic in their anatomical shape, size, and

orientation, which makes the segmentation complicated. Techni-

ques focusing on the automated segmentation of only specific

structures like the ventricular system (Schnack et al., 2001), the

thalamus and mediodorsal nucleus (Spinks et al., 2002), and

hippocampus (Crum et al., 2001) have been demonstrated. One of

the first studies for delineating multiple sub-structures in the gray

matter and white matter of brain was done using multispectral T1,

T2, proton density, Gd + T1, and perfusion imaging (Zavaljevski et

al., 2000). A complete labeling of each voxel in an MR volume of

the human brain into 37 left and right parts of neuroanatomical

structures has also been accomplished (Fischl et al., 2002). Feature

vectors from individual MR contrasts can be transformed into

statistically independent features using independent component

analysis (ICA) and subsequently used for classification (Amato

et al., 2003).

Although considerable progress has been made in automated

human brain segmentation, segmentation in the mouse brain has

received little attention. Studies that require volume analysis of

structures have used manual tracings, which are tedious and error-

prone. Consequently, methods have also relied on warping of brain

datasets into manually segmented volumes and extracting structure

labels. Diffusion tensor data have been used to distinguish white

matter structures in the mouse brain (Zhang et al., 2002); however,

an automated quantification of structure volumes has not been

attempted. The challenges to develop segmentation methods in the

mouse brain are enormous. The gray–white matter differentiation

that one sees in the human brain is much reduced in the mouse

brain, particularly at the high field required for MRH. Further, at

the high spatial resolution used for MRH, one sees much more

detail and complexity in structures, making a single approach

inefficient. Thus, there is a need for automated segmentation

methods specifically catered to the mouse brain, not only to

identify structures reliably, but also to report subtle differences in

volume and shape not visible to the human eye.

In this work, the classification of voxels in the mouse brain into

structures is accomplished by combining the MR intensity features

with spatial priors into a discriminant Bayes classifier. The spatial

priors integrate information about the location of structures in the

brain as well as the contextual relationship between structures. This

contextual information is modeled using the concept of Markov

random fields (MRFs). An MRF is a statistical model that enables

the probability of a label at a voxel location to be expressed as a

function of the labels in a predefined neighborhood of the voxel.

Modeling each neighborhood of voxels as a conditionally depend-

ent MRF with the properties of anisotropy and non-stationarity

allows the labels to be direction and location dependent. This

approach follows the algorithm developed by Fischl et al. (2002)

for automated segmentation in the human brain. There are

fundamental differences however; the technique here has been

explicitly adapted for the mouse brain where structure classes have

been chosen and inter-structure relationships have been derived

accordingly; the tissue is fixed and not live unlike the human brain

study. Finally, we use intensity information from five MR

channels. The Bayes classifier classifies each voxel into the most

probable label from the 21 structure classes defined.

Materials and methods

Tissue preparation

Six C57BL/6J male mice approximately 9 weeks in age were

used in this work.

Mice were anesthetized with an intraperitoneal injection of

pentobarbital (100 mg/kg). The animals were transcardially

perfused with an initial flush of 36 ml of 0.9% NaCl (37-C),followed by 36 ml of 10% buffered formalin. The head was then

stored overnight in formalin. Brains were excised the following

morning and stored in 0.9% saline for 3 days after which they were

scanned using the acquisition protocol outlined in the next section.

MR acquisition protocol

All imaging was performed on a 9.4-T (400 MHz) vertical bore

Oxford superconducting magnet. A 14 mm in diameter solenoid

RF coil was used. Accurate T2 weighting of tissue requires

complete recovery of the longitudinal magnetization before the

next RF excitation, thereby eliminating T1 dependence. This is

ensured with the repetition time TR of a pulse sequence set to 2–3

times the T1 of tissue of interest. In the mouse brain, tissue T1s are

of the order of 1000 ms (Johnson et al., 2002b), which makes the

ideal TR 2–3 s. For a 3D 100-Am volumetric acquisition, this

would result in a scan time of about 36 h for a spin-echo

experiment with a matrix size of 128 � 128 � 256 and NEX = 2. A

solution to achieve T2-weighted images at short TRs has been the

use of large flip angle (>90) pulses (Bogdan and Joseph, 1990;

DiIorio et al., 1995; Elster and Provost, 1993; Ma et al., 1996). Fig.

1 simulates the signal intensity curve for a conventional 90- flip

and 140- flip for different TRs in two tissues having T1 values of

1100 ms and 900 ms. Comparing the two continuous curves, we

see that the large flip angle and short TR combination ensures

higher equilibrium signal in the longitudinal axis and a faster

recovery to steady state. We also see that the T1 dependence

Table 1

MR acquisition parameters

PD T2 Diffusion

Flip angle 135 135 135

NEX 2 2 2

BW (kHz) 62.5 62.5 16.25

TE (ms) 5 30 15.52

TR (ms) 400 400 400

Matrix size 128 � 128 � 256 128 � 128 � 256 128 � 128 � 256

FOV 12 � 12 � 24 12 � 12 � 24 12 � 12 � 24

Table 2

21 structures segmented in the mouse brain

Cerebral cortex Inferior colliculus Pontine nuclei

Cerebral peduncle Medulla oblongata Substantia nigra

Hippocampus Thalamus Interpeduncular nucleus

Caudate putamen Midbrain Olfactory bulb

Globus pallidus Anterior commissure Optic tract

Internal capsule Cerebellum Trigeminal tract

Periacqueductal gray Ventricular system Corpus callosum

A.A. Ali et al. / NeuroImage 27 (2005) 425–435 427

between the two tissues that disappear using a 90- flip and long TRis also seen to diminish in the large flip angle, 140- excitation at

short TR. It is thus possible to get T2-weighted images with

optimized signal-to-noise ratio using short repetition times.

Two-dimensional imaging experiments on the formalin-fixed

mouse brain gave the maximum tissue T1 of 1100 ms. The flip

angle of 135- was arrived at by taking the supplement of the

Ernst angle with T1 = 1100 ms and a reasonable TR of 400 ms.

For a 3D acquisition, adjusting the matrix size for isotropic voxel

resolution with an effective signal-to-noise ratio of approximately

50 gave acquisition parameters indicated in Table 1. The PD- and

T2-weighted contrasts were chosen for their high contrast to noise

ratio. Diffusion-weighted datasets were included to incorporate

edge-related features in the segmentation process. These were

acquired using the Stejskal Tanner sequence with bipolar

diffusion gradients applied along the x, y, and z axes. Diffusion

gradients of 70 G/cm were used with pulse duration of 5 ms and

inter-pulse interval of 8.56 ms. The effective b value was

computed to be 2600 s/mm2.

Intra-specimen registration of brains

The MR atlas space consists of 6 mouse brains. Each image

acquisition is a multispectral registered acquisition with the

specimen kept in the same position between acquisitions with the

varied protocols. Hence inter-specimen registration is not required.

However, intra-specimen registration is required. All brains were

extracted from healthy animals belonging to the same breed, sex,

and age group. A 9-parameter affine registration that accounts for

translation, rotation, and scaling was applied to each brain. A T2-

weighted dataset from one of the specimen brains was used as a

template. The registration was done using Automated Image

Registration (AIR) (Woods et al., 1998).

Determination of the 21 structure classes in the mouse brain

Each T2-weighted dataset of the six brains was traced and

completely labeled into 21 neuroanatomical structures by two

different investigators using a standard atlas (Franklin and Paxinos,

1997) as reference. Each slice was viewed in the coronal plane and

traced in the same plane proceeding from anterior-to-posterior. All

tracings were done using an Image J software plugin (http://

rsb.info.nih.gov/ij) developed by MRPath. These tracings served

two purposes—extraction of probabilistic information for the

training data and validation of the automated segmentation. A

number of key points were kept in mind while choosing the

structure classes in the mouse brain. The brain had to be

completely labeled. Structures were chosen for their neurological

importance and the ability to visually discriminate them from

neighboring structures using the contrast available in the multi-

spectral MR protocol.

White matter structures are the easiest to identify due to their

characteristic T2 relaxation properties, which give these regions a

hypointense signal compared to surrounding areas. The corpus

callosum (CC) is the most distinct white matter structure in the

mouse brain, but this structure is extremely thin at its lateral

extremities where it borders the external capsule. Here it can be

single pixel thin, making both manual tracing and automated

segmentation a difficult task. The anterior commissure (AC), which

is a bundle of axons, shows a similar T2-weighted contrast. The

caudate putamen (CPU) is a deep gray matter area with white matter

fibers running through, which give it a characteristic texture in MR

and thus can be delineated. The superior boundary that lines the

corpus callosum is distinct and easily distinguishable from the

inferior lateral boundary. The lateral, third, and fourth ventricles are

labeled together as the ventricular system (VEN). These ventricles

are hyperintense in T2-weighted images. However, when the

cerebrospinal fluid or formalin seeps out, these regions are devoid

of signal. There is a separate ventricular class for these regions. The

two regional volumes are then combined for the final ventricular

segmentation. The periacqueductal gray (PAG) matter surrounds the

aqueduct (labeled as VEN). Other white matter structures chosen are

the optic tract (OT), the cerebral peduncles (CPED), and the internal

capsule (ICAP). The substantia nigra (SNR) anteriorly sits over the

cerebral peduncle, but ascends upwards posteriorly. In this same

deep gray matter area, the globus pallidus (GP) is also delineated.

The thalamus and hypothalamus have been segmented together as

a single thalamus (TH) class. The hippocampus (HC) is a large

structure with a distinct shape and a highly defined boundary.

Internally, the hippocampus is characterized with a multimodal

intensity distribution due to the presence of layers (granular). The

brainstem has been segmented into three regions—the midbrain

(MIDB), the pons (PON), and the medulla oblongata (MED).

Table 2 gives the complete list of 21 neuroanatomical structures

identified and segmented in the mouse brain.

Structure classification

Bayesian decision theory applies the concepts of conditional and

joint probability to the task of pattern classification. This theory uses

information from a set of training data, which is used to characterize

the probability density function of each class and also to compute

prior probabilities of the occurrence of a class. The Bayes theorem

then gives the posterior probability of an event given the data

measured. For a class k and feature vector x, this can be expressed as:

P k=xð Þ ¼ p x=kð ÞP kð Þp xð Þ ð1Þ

p(x/k) is the likelihood function that evaluates the probability of

the feature x belonging to class k, P(k) is the prior probability of a

http:\\www.rsb.info.nih.gov\ij


class, and p(x) is the marginal probability of the occurrence of

feature x. P(k/x) is the probability of the state of nature being k

given the data measured in x. This is also known as the posteriori

probability. Having evaluated this expression for different k’s, the

k that corresponds to the maximum posterior probability is the

most likely class to which the measured data belong. This is also

known as the maximum a posteriori (MAP) estimate of the

classification.

In the context of image classification, the Bayes theorem can

be used to arrive at the MAP solution for the segmentation.

The posterior probability of the segmentation can be expressed as

P(L/Y), which is the probability of label configuration L given the

multivariate intensity measure Y. The prior probability of the

configuration of labels is given by P(L). The Bayes expression can

be formulated as:

P L=Yð Þ ¼ p Y=Lð ÞP Lð Þ ð2Þ

p(Y/L) is the likelihood of observing intensity vector Y for the

label configuration L. The likelihood measure that class k

generated a feature vector Y can be computed using the multi-

variate Gaussian distribution function and the maximum likelihood

estimates of mean lk(v) and covariance matrix ~k(v) derived from

the training data as follows:

p Y½ �k L vð Þ ¼ kð Þ ¼ 1ffiffiffiffiffiffi2p

pðk~

k

vð ÞkÞd=2

� exp� 1

2Y½ � vð Þ � lk vð Þð ÞT~

k

vð Þ�1Y½ � vð Þ � lk vð Þð Þ

� ��ð3Þ

p([ Y]kL(v) = k) is the probability of observing image intensity

vector Y if the label at v belonged to class k. d is the dimensionality

of the feature space. Intensity data from T2, proton-density, and

diffusion-weighted (x, y, and z gradients) acquisition at each voxel

form a five-dimensional feature vector [ Y1, Y2, Y3, Y4, Y5]v. The

exponential term in Eq. (3) is the Mahalanobis distance measure.

The intensity distribution of each structure class Fk_ is modeled as a

multivariate Gaussian at each location Fv_ in the atlas. The

Gaussian is characterized by mean lk(v) and covariance matrix

Rk(v) specified for each atlas location. Means and covariance

estimates can be computed as follows:

lk vð Þ ¼ 1

N~N

i¼1

Y1; Y2; Y3; Y4; Y5½ � vð Þ ð4Þ

~k

vð Þ ¼ 1

N � 1~N

i¼1

Y1; Y2; Y3; Y4; Y5½ � vð Þ � lk vð Þð Þ

� Y1; Y2; Y3; Y4; Y5½ � vð Þ � lk vð Þð ÞT ð5Þ

From Eqs. (4) and (5) we see that the class statistics vary with

location. This accounts for the multimodal nature of intensity

histograms in structures, where a single mean and covariance

matrix cannot adequately represent the data. Thus in the

classification step, intensity values at each voxel are evaluated

only against means and covariance values that describe the MR

intensity distribution data of structures occurring at that location in

the atlas. N is the number of manually labeled training sets that

make up the atlas.

lk(v) and ~k(v) are the maximum likelihood estimates of

structure class k at every location in the atlas and form the

multivariate intensity model for classification.

The P(L) term in the Bayes expression has to incorporate both

the location and the contextual priors of structure labels at every

location. Location prior is the prior probability of the occurrence of

a label at a voxel location. For every voxel location in the atlas, the

prior probability of a structure k occurring at location v was

computed. For a voxel at location Fv_, the location prior is

evaluated as the ratio of the number of times class k occurs at Fv_ tothe number of times a brain voxel is mapped to Fv_ in the training

datasets. This is computed as:

p L vð Þ ¼ kð Þ L vð Þ ¼ kð Þvox

ð6Þ

where L(v) indicates the label at location v in the atlas.

The contextual priors are estimated for each voxel location with

the adjoining 6 nearest voxels in a first order neighborhood system.

For a voxel at location v, the first order neighborhood system can

be described as Nv = {vn, vs, ve, vw, vf, vb}. vn, vs, ve, and vw are

the neighboring voxels in the 4 cardinal directions in the plane. vfand vb are the voxels in front and behind the plane, respectively.

Pairwise probabilities are computed for every combination of

structure pairs between the voxel under consideration and the six

neighboring voxels taken one at a time in the first order

neighborhood:

P vð Þkikj ¼vox L vð Þ ¼ kikL Nvð Þ ¼ kj

� �vox L vð Þ kif g ð7Þ

In Eq. (7), P(v)kikj is the probability of observing the class

ki at location v when class kj occurs at a voxel in Nv. A

histogram function computes the number of times the particular

label configuration has occurred; probabilities are then stored in

a K � K matrix for every atlas location v for all 6 directions

around the voxel of interest. K is 22 for 21 structure classes and one

miscellaneous class. Currently, the information is stored at a scale of

90 Am and is averaged in a 3 � 3 grid around each voxel.

Contextual priors are modeled using the concepts of Markov

random field (MRF) theory. A particular MRF model favors the

arrangement of labels that conform to itself by associating them

with larger probabilities (Li, 2001). A set of random variables or

fields L can be defined for an image domain S where L is a label

arrangement. Each field is a subset of the image S and its elements

can take on specific configuration of labels. L can be modeled as a

Markov random field at a location v with respect to its selected

neighborhood Nv and will satisfy the following two criteria:

P Lð Þ > 0 ð8Þ

P Lv kLS�vð Þ ¼ P Lv kLNvð Þ ð9Þ

where (S � v) is the set of all voxels in the image excluding the

voxel under consideration at v. The prior probability of the label

at voxel location v being dependent on remaining voxels in an

image is equivalent to it being dependent only on the labels of

voxels in a predefined neighborhood Nv. This is the Markov

property. P(L) now, in this case, is the joint probability of the

MRF characterized by the label pattern L. P(LvkLS�v) is the

local conditional probability at voxel v. An MRF can be

evaluated globally using the joint probability or locally using

conditional probabilities. Furthermore, the equivalence between

Fig. 2. Proton density (PD), T2, and diffusion-weighted images from

registered 3D multispectral acquisition of formalin-fixed mouse brain at

level of the thalamus (left column) and midbrain (right column).


the MRF conditional probability and the Gibbs joint probability

distributions enables the probability of the MRF to be expressed

as an energy function of many local Fclique_ potentials. A clique

is a subset of voxels in the image domain. The first order

neighborhood Nv of a voxel can thus be represented as a clique

and the clique potentials as a combination of the probabilities of

labels at each location given their neighborhood. Applying the

Fig. 3. Automated segmentation results in a slice from 3D MRM of the mouse br

defined labels.

Markovian property, the local conditional dependence can be

expressed as:

p L vð ÞkL S � vð Þð Þ ¼ p L vð ÞkL v1ð Þ; L v2ð Þ; L v3ð Þ; N ; L við Þð Þ; viZNv

ð10ÞThis reduces the global classification task in an image to a set of

smaller local classifications where the prior probability of a label at a

voxel location v is affected only by the labels at voxels defined for a

neighborhood Nv and independent of the neighborhood of other

voxels. As a result, the prior probability of a segmentation pattern

P(L) can be expressed as the product of local conditional

probabilities of each voxel with its neighborhood:

p Lð Þ ¼ PvZS

p L vð ÞkL v1ð Þ; L v2ð Þ; L v3ð Þ; N ; L við ÞÞ; viZNvð ð11Þ

Applying Bayes rule on the right-hand side of Eq. (11), the

conditional probabilities can be interchanged with the product of

the location prior at each voxel and the conditional probability of

labels in a neighborhood given the label at the center voxel:

p Lð Þ” PvZS

p L vð Þð Þp L v1ð Þ; L v2ð Þ; N ; L við ÞkL vð ÞÞ; viZNvð ð12Þ

Fischl et al. (2002) have simplified Eq. (12) by assuming only a first

order dependence between labels in a neighborhood in which case

the probability of a label given the labels in a neighborhood can be

computed by taking the product of the conditional probabilities with

respect to each of the neighboring labels at a time. The expression

can be written as:

p Lð Þ” PvZS

p L vð Þð Þ Pk

i¼1p L við ÞkL vð Þ; við Þ ð13Þ

Eq. (13) evaluates the prior probability of a segmentation pattern L

and incorporates both the location priors for each structure class at

each location v and the contextual priors between structures at

different voxels. Applying the iterated conditional modes algorithm

(Besag, 1986) for a faster convergence to the MAP solution of the

equation, the segmentation is initialized using only the multivariate

intensity model. Using this initial estimate, the segmentation label at

each voxel is iteratively updated by estimating the label that

maximizes the conditional posterior probability given the condi-

tional density of the multivariate intensity vector and the current

label estimate at each voxel.

The complete expression is given as follows:

arg max p L vð Þ ¼ k kL við Þ; Y½ � vð Þ; við Þ¼ p Y½ � vð ÞkL vð Þ ¼ kð Þp L vð Þ ¼ kð Þ P

k

i¼1p L við ÞkL vð Þ¼ k; við Þ ð14Þ

p([ Y](v)kL(v) = k) is the conditional probability of observing

an intensity vector Y for a particular class k. p(L(v) = k) is

ain. (A) Gray scale, (B) automated segmentation results, and (C) manually

Fig. 4. Automated segmentation results in a slice from 3D MRM of the mouse brain. (A) Gray scale, (B) automated segmentation results, and (C) manually

defined labels.


the prior probability of the voxel at v being labeled as

belonging to structure k. Pi=1

kp(L(vi)kL(v) = k,vi) is the joint

conditional probability given the current estimate of labels at

location v and vi where viZNv. Eq. (14) is evaluated until no

Fig. 5. 3D volume-rendered structures automatically segmented and overlaid on M

ant—anterior, pos—posterior, rt—right, lt—left.

more voxels change labels. This is followed by a post-processing

step of connected component labeling. Isolated groups of voxels

are relabeled according to the majority of labels in their

neighborhood.

RM volume of mouse brain in dorsal (A), ventral (B), and sagittal (C) view;


Results and discussion

MR acquisition

Six mouse brains were scanned using the acquisition protocol

outlined in Table 1. A total of 30 3D volume datasets were

obtained with five contrasts; PD weighting, T2 weighting, and

diffusion weighting (x, y, z) per brain. Slices at two different

coronal levels in the brain are displayed in Fig. 2. The left column

is at the level of the thalamus and the column at the right is in the

midbrain area.

Segmentation and validation

The algorithm was evaluated on each of the six mouse brain

volumes. Probabilistic information containing multivariate inten-

Fig. 6. 3D volume-rendered structures automatically segmented and overlaid o

Magnified view; ant—anterior, pos—posterior, rt—right, lt—left.

sity distributions and the local and contextual priors is extracted

and stored for each brain. However, for the segmentation of each

brain, only the data from the remaining five volumes are used as

part of the training set. Thus, the test volume was kept out of the

atlas set. Each brain was segmented automatically and then

compared with results from its manual tracings. Segmentation by

two raters was used for computing the inter-rater reliability metrics.

Figs. 3 and 4 illustrate the labels for a slice from 3D volumetric

data of a mouse brain from automated and manual segmentation.

By visual inspection, we see that the segmentation works well even

in areas of complicated neuroanatomy where a number of

structures are present in close vicinity to each other. This is

illustrated in Fig. 4 where the hippocampus first appears anteriorly.

In the coronal slices of this area we find from medial to lateral a

close proximity of the hippocampus, lateral ventricles, caudate

putamen, and inferiorly the thalamus, internal capsule, and globus

n MRM volume of mouse brain in dorsal (A) and ventral (B) view. (C)


pallidus. All structures here are segmented in a spatial config-

uration consistent with the manual segmentation. Misclassifica-

tions of voxels predominantly take place at the boundaries between

different structures, which is a result of partial volume effects. Figs.

5 and 6 give 3D volume-rendered visualization of the segmented

structures overlaid in an MR volume generated using Volocity

software (http://www.bucher.ch/improvision/volocity.html). The

opacity of the MR volume has been adjusted to show structures

inside the brain. In each set, a selection of structures is highlighted

with the others turned off. Highlighted structures in Fig. 5 include

the cerebellum, inferior colliculus, thalamus, ventricular system,

midbrain, cerebral peduncles, pontine nuclei, globus pallidus,

internal capsule, and the substantia nigra. Fig. 6 shows the

ventricular system, caudate putamen, periacqueductal grey, hippo-

campus, internal capsule, and globus pallidus. Fig. 6C illustrates

the ventricular system and associated structures magnified.

Fig. 7. Reliability of automated segmentation with manual tracing for 21 structures

structures. Bottom: percent difference overlap for same structures. Key: CORTEX

caudate putamen, GP—globus pallidus, ICAP—internal capsule, PAG—periacque

thalamus, MIDB—midbrain, AC—anterior commissure, CBLM—cerebellum, V

INTPEDN—interpeduncular nucleus, OLFBLB—olfactory bulb, OPT—optic trac

The segmentation was statistically validated using morphom-

etry indices of percent volume overlap and percent difference

overlap. Letting LM and LA indicate labeling of a structure from the

manual tracing and automated segmentation, respectively, the

volume overlap percentage between them for a structure can be

computed using the equation:

O LA kLMð Þ ¼ V LA7LMð ÞV LAð Þ þ V LMð Þð Þ=2 � 100 ð15Þ

The intersection term in the numerator makes this metric sensitive

to differences in spatial location to a greater extent than differences

in volume or size. This is also known as the j index or the Dice

coefficient. Higher values of this metric indicate greater conformity

between the automated and manual segmentation. The overlap

percentage is not influenced by structure size or sample size and,

. Error bars are standard errors of the mean. Top: percent volume overlap for

—cerebral cortex, CPED—cerebral peduncle, HC—hippocampus, CPU—

ductal gray, INFC—inferior colliculus, MED—medulla oblongata, THAL—

EN—ventricular system, PON—pontine nuclei, SNR—substantia nigra,

t, TRI—trigeminal tract, CC—corpus callosum.

http:\\www.bucher.ch\improvision\volocity.html

Table 3

Results comparing automated segmentation in mouse brain with human

brain

Structure Percent

overlap

(mouse)

Percent

overlap

(human)

Hippocampus 87 80

Thalamus 93 87

Caudate putamen 88 90

Ventricular system 70 90


thus, is a good measure of efficacy of the segmentation especially

in our case.

Volumetric differences between labelings independent of the

spatial position can be verified by a difference function given

as:

D LA; LMð Þ ¼ kV LAð Þ � V LMð ÞkV LAð Þ þ V LMð Þð Þ=2 � 100 ð16Þ

Fig. 7 illustrates the percent volume overlap and difference

overlap between the automated segmentation and manual tracing

for the 21 structures. The algorithm does exceedingly well for

large structures like the cortex, hippocampus, caudate putamen,

medulla oblongata, thalamus, midbrain, inferior colliculus, and

cerebellum (80–90%). Small and thin structures like the anterior

commissure, optic tract, and trigeminal tract are penalized for their

size (50–60%). The corpus callosum is large in size, but its width

decreases to single pixel laterally where it lines the external

capsule and hence does not segment out as well (70%). The

ventricles suffer from high variability in their size and shape in the

excised brains, which could be due to perfusion and handling

differences. In cases where fluid seeps out of the ventricle after

perfusion, the ventricles are hypointense and collapsed. Fluid

retained in the ventricles makes them appear expanded and

hyperintense with contrast. Results for deep cortical structures like

the internal capsule, globus pallidus, periacqueductal grey, and

substantia nigra are encouraging. The difference overlap indicated

the difference in actual volume measurements. All values here are

sufficiently low, which indicate good agreement between manual

and automated segmentation. Fig. 8 gives the inter-rater reliability

from percent volume overlap of manual segmentations from the

two raters. Table 3 shows the percent volume overlap measures

comparing results for the mouse brain with those seen using the

same technique (Fischl et al., 2002) in human brain studies. While

the sample dataset (n = 6) is limited, results indicate that we will

be able to autosegment the mouse brain with accuracy comparable

to that achieved now in human studies. The software code

currently executes the automated segmentation of a 90-Am

Fig. 8. Inter-rater reliability for manual segmentation for the 21 structures. Key: C

CPU—caudate putamen, GP—globus pallidus, ICAP—internal capsule, PAG—p

THAL—thalamus, MIDB—midbrain, AC—anterior commissure, CBLM—cereb

nigra, INTPEDN—interpeduncular nucleus, OLFBLB—olfactory bulb, OPT—op

resolution mouse brain dataset in 15 min on a 2.5-GHz Macintosh

system.

Conclusions and future work

We have successfully implemented automated segmentation of

the complete mouse brain into 21 structures, including sub-cortical

structures like the corpus callosum, hippocampus, internal capsule,

globus pallidus, periacqueductal grey, and substantia nigra. The

algorithm is trained using multispectral MR intensity data, prior

probabilities of the spatial location of structures, and inter-structure

contextual relationships. The use of T2, PD, and diffusion-weighted

contrasts enables the algorithm to incorporate characteristics from

multiple channels in the classification. Although a linear registration

has been used in this work, the application of higher dimensional and

non-linear registration techniques would improve the segmentation.

This is especially relevant when the genetic mutation used severely

affects the anatomical relationships in the resultant mouse model.

The current work achieves a segmentation reliability of 70–90%

between manual and automated segmentation.

Though the current work uses multiple MR contrasts, it would

be useful to determine the contribution of each contrast in the

segmentation. A future directive is to quantify the contribution of

each MR channel in the segmentation and estimate an optimum

subset of contrasts required. The contrasts can be further optimized

ORTEX—cerebral cortex, CPED—cerebral peduncle, HC—hippocampus,

eriacqueductal gray, INFC—inferior colliculus, MED—medulla oblongata,

ellum, VEN—ventricular system, PON—pontine nuclei, SNR—substantia

tic tract, TRI—trigeminal tract, CC—corpus callosum.


for the segmentation of a specific structure or a group of structures.

The 90-Am resolution used in this work is still at the lower end for

studying detailed neuroanatomy in the mouse brain. The average

width of the granular layer in the mouse brain hippocampus is, for

example, 30 Am, making high resolution indispensable. An

isotropic resolution of 10–15 Am is what a neuroanatomist would

like to look at for effectively extracting histologically relevant

information from MR microscopy. Recently, 20-Am resolution has

been attained with the use of novel Factive staining_ techniques thatinvolve perfusion fixation combined with a T1 reducing contrast

agent (Johnson et al., 2002a). Due to the high signal-to-noise ratio

gained by T1 shortening agents and the enhanced resolution,

structure that we do not normally see in the formalin-fixed brain

appears in exquisite detail in the actively stained brain. Thus, the

next step is the segmentation of structures in the higher resolution

MRM of the mouse brain. The high resolution might enable us to

further subdivide already segmented structures into constituent

components. Ultimately, this would enable robust and expedited

morphometry analysis of structures to aid in the morphological

phenotyping of mouse models.

Acknowledgments

The authors thank Boma Fubara and Will Kurylo for their

contribution. All work was performed at the Duke Center for In

Vivo Microscopy, an NCRR/NCI National Resource (P41

RR05959 and R24 CA92656). This work was made possible

through collaborations with the Mouse Bioinformatics Research

Network (MBIRN).

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