AutoSeg 2.1 Documentation
Authors: Clement Vachet, Emilie Perrot, Clementine Ouziel
contact: cvachet@unc.edu
Neuro Image Research and Analysis Laboratories
UNC Chapel Hill – May 24th, 2011
Table of Contents
Introduction ............................................................................................................................................................. 4
1 Tutorial .................................................................................................................................................................. 4
1.1 Computation .................................................................................................................................................. 4
1.1.1 Set the process data directory ................................................................................................................ 4
1
1.1.2 Set data information .............................................................................................................................. 4
1.1.3 Set output data directory ....................................................................................................................... 4
1.1.4 Set the dataset ........................................................................................................................................ 5
1.1.5 Computation option ............................................................................................................................... 7
1.1.6 Compute automatic segmentation ......................................................................................................... 7
1.2 Parameters ..................................................................................................................................................... 8
1.2.1 Set the atlases ........................................................................................................................................ 8
1.2.2 Set the structures to be segmented ........................................................................................................ 9
1.2.3 Set advanced parameters ..................................................................................................................... 10
1.3 Regional histogram option .......................................................................................................................... 11
1.4 Menu options ............................................................................................................................................... 13
1.5 Exit .............................................................................................................................................................. 13
1.6 AutoSeg outputs .......................................................................................................................................... 14
1.6.1 Quality control: MRML Scene ............................................................................................................ 14
1.6.2 Skull-stripped intensity rescaled image ............................................................................................... 14
1.6.3 Regions of interest ............................................................................................................................... 14
1.6.4 Volume analysis files .......................................................................................................................... 14
1.6.5 Regional histogram option: MRML Scenes ........................................................................................ 14
2 FAQ ..................................................................................................................................................................... 15
2.1 How to improve the rigid registration step? ................................................................................................ 15
3 Automatic segmentation method ......................................................................................................................... 16
3.1 Atlas creation............................................................................................................................................... 16
3.1.1 Unbiased average image creation ........................................................................................................ 16
3.1.2 Subcortical structures creation ............................................................................................................ 16
3.2 AutoSeg pipeline ......................................................................................................................................... 16
3.2.1 Bias field correction ............................................................................................................................ 17
3.2.2 Rigid registration to a common coordinate image .............................................................................. 17
3.2.3 Tissue segmentation ............................................................................................................................ 17
3.2.4 Skull-stripping ..................................................................................................................................... 18
3.2.5 Loop .................................................................................................................................................... 18
3.2.6 Intensity rescaling ............................................................................................................................... 18
3.2.7 Atlas to case registration ..................................................................................................................... 19
3.2.8 Applying the transformations .............................................................................................................. 19
4 Outputs location................................................................................................................................................... 20
4.1 Processing data directory............................................................................................................................. 20
4.1.1 Files ..................................................................................................................................................... 20
4.1.2 AutoSeg_Volume ................................................................................................................................ 20
4.1.3 AutoSeg_MRML................................................................................................................................. 20
4.2 Data directory .............................................................................................................................................. 20
4.2.1 AutoSeg ............................................................................................................................................... 20
4.2.2 AutoSeg/BiasFieldCorrected ............................................................................................................... 21
4.2.3 AutoSeg/atlasIso.................................................................................................................................. 21
4.2.4 AutoSeg/ems{_N}............................................................................................................................... 21
4.2.5 AutoSeg/Stripped ................................................................................................................................ 22
4.2.6 AutoSeg/WarpROI .............................................................................................................................. 22
4.2.7 AutoSeg/MRMLScene ........................................................................................................................ 24
5 Screenshots .......................................................................................................................................................... 25
6 Credits ................................................................................................................................................................. 36
Introduction
AutoSeg is a tool allowing the segmentation of probabilistic sub-cortical structures and label maps, such
as generic ROI maps and parcellation maps. The approach is a fully automatic segmentation via a deformable
registration of an unbiased diffeomorphic atlas with probabilistic spatial priors.
This software executes a BatchMake script and runs several tools as threads, with the possibility to
process datasets locally or on a distributed environment using Condor.
1Tutorial
This tutorial explains how to use AutoSeg through the FLTK graphic user interface.
1.1Computation
This directory will contain BatchMake scripts, eventually quality control images and volume analysis files.
1.1.1Set the process data directory
Type (T1-weighted, T2-weighted, PD) of the source images need to be set.
1.1.2Set data information
Type (T1-weighted, T2-weighted, PD) of the source images need to be set.
1.1.3Set output data directory
Under each data directory, a directory (named 'AutoSeg' by default) will be created to store the output files. One
can change the name of this directory, if several studies are performed on the same dataset (e.g by playing with
parameter settings).
1.1.4Set the dataset
Dataset with several formats can be processed: GIPL format, meta format (.mhd), NRRD format,
analyze format (.img).... As AutoSeg runs several programs which work best with NRRD images, output images
will be saved in NRRD.
A browser displays data that are about to be processed, the user being able to delete selected lines
('Remove' button) or to clear entirely the browser. If the user works with multi-modal images, the browser will
then display data separated by interactively resizable columns.
The data selection can be done automatically and/or manually:
Automatic data selection
One can edit the data automatically by setting a general data directory and a filter indicating what files
are about to be processed (e.g T1/*T1.nrrd). Starting from the data directory, the tool will find recursively all
files that match the first given expression, corresponding to the T1-weighted images. The other expressions, in
order to find the T2-weighted and/or PD images, are expressions relative to the path of the T1-weighted images
(e.g ../T2/*T2.nrrd). Clicking on the 'Refresh filelist' button will display the dataset in the browser.

Manual data selection
The user has also the possibility to add data manually by clicking on the 'Add' button. A pop up window
will then appear, in which data location need to be set.

1.1.5Computation option
3 options can be selected:
 Compute volume analysis: this option allows the user to start a volume analysis of the computed
subcortical structures and/or label maps.
 Compute cortical thickness: this option will computed voxel-based regional cortical thickness
measurements. If a parcellation map is provided, a lobar analysis will be performed.
 Recompute all: this option will recompute the segmentation for the whole dataset, deleting
previous results.
 Use condor: this option allows the user to compute the dataset in a distributed environment via
Condor, using several computational resources. BatchMake converts its script to a condor script
ready to be sent to the Condor manager.
1.1.6Compute automatic segmentation
Pressing the 'Compute AutoSeg' button runs a BatchMake script to process the dataset, which can
eventually be stopped by pressing the 'Stop' button.
AutoSeg will check whether or not a study has already been performed in this directory (by considering
the existence of the 'AutoSeg_Parameters.txt' file). One can process the dataset with the current parameter
settings (if new subjects have been added) or cancel the execution. If one wants to perform a new study on the
same dataset, by using different parameter settings, best would be to consider a new AutoSeg processing
directory. Output files (parameter settings, volume analysis) would then be accurate.
When the automatic segmentation process starts, a pop up window appears, displaying the status of the
current segmentation pipeline. This pop up window can be displayed ('Show process status') or hidden to the
convenience of the user.
1.2Parameters
Several parameters need to be set before starting the computation. A default parameter file is loaded
when AutoSeg starts. However, these parameters can be set manually, in order to select the structures to be
segmented, or eventually use a different atlas.
1.2.1Set the atlases
Set the common coordinate image
The source images will be rigidly registered to this atlas and thus will be in the same reference
coordinate space. The type of the common coordinate image has to be set.
The common coordinate image should have the same type than the region of interest atlas.

Set the tissue segmentation atlas directory
During the pipeline, a tissue segmentation step will be applied in order to get 3 labels from the brain:
White Matter, Grey Matter, CSF. Depending on the source images, this segmentation will be a one-channel
(using T1 image), two-channel (T1&T2 or T1&PD), or three-channel (T1&T2&PD). The type of the tissue
segmentation image has to be set, the image should be called 'template.nrrd'.

Set the ROI atlas file
Set the T1-weighted ROI atlas, where the atlas probabilistic subcortical structures and label maps have
been segmented.

1.2.2Set the structures to be segmented
Parcellation maps
The user can compute brain parcellations.
Using the “soft” option will calculate volume statistics of the parcellation by considering tissue segmentation
probability maps, whereas “hard” option will use the hard tissue segmentation label map.

Probabilistic subcortical structures
Twelve subcortical structures can be set or selected (left and righ): amygdala, caudate, hippocampust, pallidus,
putamen.

Generic ROI maps
One can also add ROI maps:

1.2.3Set advanced parameters

Rigid Registration
By default, a rigid registration, which can be disabled, is performed to the input images, which includes regriding. The grid template is by default the region of interest atlas. The grid template is only a template image to
set size and spacing of the outputs images.
Using the default region of interest atlas, these parameters are:

size: 170x205x170

spacing: 0.9375*0.9375*0.9375
Depending on the input images, the user can directly set the grid template information, which is needed
if the inputs images are larger (considering the spacing) than the region of interest atlas. Otherwise, the brain
may be cut in the output images. It is also advised to use an isotropic spacing.

Tissue segmentation and warping parameters
These parameters may eventually be modified, but a non-expert user doesn't need to change them.
The first set of parameters is related to the tissue segmentation step. Depending on the tissue atlas
description, different well-suited parameters will be used to improve the segmentation accuracy. One can use
ABC, which performs a fluid registration.
The second set of parameters is related to the deformable registration step.

Skull-stripping
One can add the option 'delete vessels' if necessary. T1 images with high intensities which correspond to vessels,
may affect the atlas warping. Such voxels will be replaced by a gaussian smoothed values (size 2).

Intensity rescaling
Intensity rescaling needs to be performed prior to the atlas warping. One can use histogram quantile matching
(default) or tissue mean matching.
1.3Regional histogram option
The regional histogram option allows a histogram analysis by providing auxiliary datasets, such as DTI
images.
To use this option, the user first has to select the type of the source images, in the second AutoSeg tab. These
images have been processed in the main computation, so it can be a T1, T2 or a PD image; in the atlas space, or
bias corrected or skull-stripped image.
Then, the type of the transformation (rigid,affine or B-Spline) need to be selected. The previously selected
source selection is registered to the first auxiliary image. We suppose that other auxiliary images are pre-
registered to the first auxiliary image.
The auxiliary data information then needs to be set. The user can use different types of auxiliary images
(FA, MD, B0). He has to precise it by selecting the corresponding check button and writing the type in the text
zone at the right of the check button. Thus, directories will be created for each type of auxiliary images, whose
name correspond to the text in the text zones.
Next, the user needs to set the dataset. The data selection works as in the first tab. For the automatic auxiliary
data selection, the user set the filters relative to the source images, files must be located in the data directory.
Source images are automatically obtained from the data selection in the computation tab.
Finally, the user can set several parameters in the advanced parameters tab. The first one is quantiles (values by
default are 1,5,33,50,66,95,99) and the second one is the point spacing, used only with a B-Spline registration
(the default value is 10 mm).
1.4Menu options
AutoSeg contains two default parameters files:
. AutoSeg_DefaultSharpAtlasParameters.txt: default parameter file for sharp tissue atlas
. AutoSeg_DefaultFuzzyAtlasParameters.txt: default parameter file for fuzzy tissue atlas
When AutoSeg starts, it loads the 'AutoSeg_DefaultSharpAtlasParameters.txt' file.
The menu helps to deal with parameter files and default ones. The available options are:
. Load Computation file:
Load a computation file. In order to re-process a study, one can
directly load this file instead of setting again all the computation information.
. Load Parameter file:
Load a parameter file. In order to re-process a study, one can
directly load this file instead of setting again all the parameters information.
. Save Computation file:
Save a computation file
. Save Parameter file:
Save a parameter file
. Use default sharp atlas parameters:
Use default sharp atlas parameters as current parameters
. Use default fuzzy atlas parameters:
Use default fuzzy atlas parameters as current parameters
. Set default sharp atlas parameters:
Save current parameters as default sharp atlas parameters
. Set default fuzzy atlas parameters:
Save current parameters as default fuzzy atlas parameters
. Reset default sharp atlas parameters: Reset default sharp atlas parameters
. Reset default fuzzy atlas parameters: Reset default fuzzy atlas parameters
1.5Exit
If one wants to exit the tool while the process is still running, one has the possibility to stop the current
process or to quit AutoSeg without stopping the pipeline, thus continuing the automatic segmentation in the
background.
1.6AutoSeg outputs
1.6.1Quality control: MRML Scene
Quality control is provided via 3D Slicer MRML Scenes. A MRML scene is created per subject,
containing a snapshot for each step of the pipeline. Upon completion of the pipeline, clicking on the 'Show
MRML Scene' button generates a pop up window giving the choice of starting Slicer3 and automatically loading
the MRML Scene of the first case. This allows the user to quickly check the quality of the segmentation pipeline,
and see immediately if there has been a problem on one or several cases during the process.
1.6.2Skull-stripped intensity rescaled image
For each data, a skull-stripped intensity rescaled image is computed. This is the image the atlas is
registered to. Using other tools, such as itkSNAP (http://www.itksnap.org), one can display this image.
1.6.3Regions of interest
If several subcortical structures have been selected, a file gathering all the ROIs is computed. Not only
this file but also label maps, such as parcellation maps and generic ROI maps can be overlayed to the skullstripped intensity rescaled image to check the accuracy of the automatic segmentation.
1.6.4Volume analysis files
If the 'Compute volume' option is selected the tool computes a volume analysis. A subdirectory
'AutoSeg_Volume' with related result files is created in the Process Data directory.
4 volumes analyses can be computed depending on the selected options:
 Tissue Segmentation volume analysis:
Volumes of the White Matter, Grey Matter, CSF.
 Subcortical structures volume analysis:
Volumes of the selected subcortical structures
 Generic ROI Map volume analysis:
WM,GM,CSF volumes for each label
 Parcellation Map volume analysis:
WM,GM,CSF volumes for each label
1.6.5Regional histogram option: MRML Scenes
To allow the user to perform a quality control, four MRML scenes are created. They are stored in the
MRML directory, in the process data directory. The BatchMake scripts used to create these MRML scenes are
stored in the process data directory.
-Source_MRMLScene.mrml
This MRML scene contains one snapshot for each source case. The snapshot displays two images, the
background image which is the registered source, and the foreground, the first auxiliary image. The user can
control with that scene the success of the affine, rigid or B-Spline registration.
-Parcellation_MRMLScene.mrml
This MRML scene contains one snapshot with two images by couple source case/auxiliary image. The
background image is the registered source and the label image is the registered parcellation.
-Struct_MRMLScene.mrml
This MRML scene contains one snapshot by triplet source case/auxiliary image/subcortical structure. The
background image is the registered source and we have also the registered subcortical structure as a label image.
-AllROI_MRMLScene.mrml
The AllROI MRML scene contains one snapshot by couple source case/auxiliary image. The background image
is still the registered source and the label is the registered AllROI image.
2FAQ
2.1How to improve the rigid registration step?
If the rigid registration to a common coordinate image is not correct, or the related image is empty, first
check the orientation of the input images. If the orientation is correct, a manual registration can be computed to
initialize the automatic one.
In this case, the user needs to run Slicer3 and load the common coordinate image (atlas), as well as the
bias field corrected image and the transform output in the alasISO folder (T1Image_out.txt).
Apply the transform to the bias field corrected image and use the module Transforms to give manually to the
result the same orientation as the atlas.
The transformation file should be saved in the 'atlasIso' directory as follow: T1Image_init.txt
Then the user needs to remove all the other files in the 'atlasIso' directory and the other directories
(BiasFieldCorrected, ems{_N}, Stripped, WarpROI, MRMLScene), and run AutoSeg again: AutoSeg will then
recognize the previously saved transformation file and will use it as an initialization for the rigid registration
step.
3Automatic segmentation method
Our approach is a fully automatic segmentation via a deformable registration of an unbiased atlas with
probabilistic spatial priors.
The following pages will describe the atlas creation (the package already containing the atlas) and the
automatic segmentation pipeline itself: this is what is computed each time a user runs the software.
3.1Atlas creation
Step 2
Step 1
T3
T3
T2
T4
T2
T5
T1
T4
T5
T1
3.1.1Unbiased average image creation
The current atlas, used by default in AutoSeg has been build from a dataset of 20 T1 images, on which
subcortical structures have been manually segmented. The atlas building consists in computing an average image
along with transformation fields mapping each training case to the average image (Step 1).
3.1.2Subcortical structures creation
These transformation fields are then applied to the manually segmented structures of each case in order
to obtain a probabilistic map on the atlas (Step 2).
3.2AutoSeg pipeline
Input images:
3.2.1Bias field correction
Tool: N4BiasFiledCorrection
3.2.2Rigid registration to a common coordinate image
All cases are rigidly registered (6 parameters: translation and rotation) to a common coordinate image, in order
to be in the same coordinate space. If the dataset is composed of multi-modality images, all images are aligned.
Tools used: BRAINSFit and ResampleVolume2
3.2.3Tissue segmentation
Depending on the source images, a 1, 2, 3-channel expectation-maximization tissue segmentation is
computed for each case. The outputs are:



a filter, intensity inhomogeneity corrected image
a label image: white matter (WM), grey matter (GM), cortical spinal fluid (CSF)
the probabilistic maps of WM, GM, CSF.
Label image
WM
GM
CSF
3.2.4Skull-
Using the label image from the previous step, our registered image is skull stripped.
stripping
3.2.5Loop
If 'Do Loop' is selected with N iterations, there will be N more “Tissue segmentation followed by Skullstripping”.
3.2.6Intensity rescaling
The stripped image is then intensity rescaled to match the atlas.
3.2.7Atlas to case registration
The atlas needs now to be registered to each case, mapping the gray level intensities. This is done
through two steps.
Atlas
TA
TW
Affine registration
An affine registration TA(15 parameters: translation, rotation, scaling, skews) is computed from the atlas
to the skull stripped image. The registration is computed with a conjugate gradient descent optimization and a
cubic spline interpolation.

Tool used: RView
Deformable registration
A fluid registration TW, with a linear interpolation, is computed from the previous affinely registered
image to the skull stripped image.

Tool used: WarpTool
3.2.8Applying the transformations
Tool used: RView, WarpTool
Subcortical structures
 Probabilistic maps creation
The affine transformation is applied using a cubic spline interpolation, whereas the warping is done with
a linear interpolation.

 ROI thresholding
In order to get hard segmentations, all probabilistic maps are thresholded to half of their maximum intensities.
Lateral ventricles
As lateral ventricles have a wide shape distribution across subjects, a correct probability map is hard to
obtain. Large binary masks are used instead, on which the previously computed affine transformation is applied
using a nearest neighbor interpolation, then the warping. As their segmentation is not precise enough, the CSF
probability map (obtained during the tissue segmentation step) is masked by these ventricle binary images,
which creates a probabilistic map for each ventricle.

ROI gathering
Subcortical structures and lateral ventricles are gathered into a single file, allowing an easier quality control.
 Label maps
The affine and fluid deformations are applied to the label maps defined in the atlas, such as generic ROI
maps and parcellation maps.

4Outputs location
4.1Processing data directory
4.1.1Files
If the automatic data selection is computed:
-AutoSeg_Data.bms:
BatchMake script to compute data automatically
-AutoSeg_Data.txt:
Text file containing all the data to be computed
-AutoSeg.bms:
BatchMake script
-AutoSeg.log
Log File
-AutoSeg_Computation.txt:
Computation file
-AutoSeg_Parameters.txt:
Parameter file
4.1.2AutoSeg_Volume
In this directory are stored files related to the volume analysis:
-AutoSeg_GenericROIMapVolume.csv:
Generic ROI map volume analysis file
-AutoSeg_ParcellationMapVolume.csv:
Parcellation map volume analysis file
-AutoSeg_SubcorticalStructureVolume.csv:
Subcortical structures volume analysis file
-AutoSeg_TissueSegmentationVolume.csv:
Tissue segmentation volume analysis file
4.1.3AutoSeg_MRML
In this directory are stored MRML Scenes:
-Image_MRMLScene.mrml
MRML Scene
4.2Data directory
Considering your input images are called: T1image, T2image and PDImage
-{T1,T2,PD}Image.nrrd
NRRD images
4.2.1AutoSeg
-AutoSeg_Computation.txt:
Computation file
-AutoSeg_Parameters.txt:
Parameter file
4.2.2AutoSeg/BiasFieldCorrected
-{T1,T2,PD}Image_BiasFieldCorrected.nrrd : bias field corrected image
4.2.3AutoSeg/atlasIso
-{T1,T2,PD}Image_BiasFieldCorrected_out.txt:
-T1Image_BiasFieldCorrected_regAtlas.nrrd:
Log files related to the rigid registration step
T1 images registered to the common coordinate image
-{T2,PD}Image_regT1_BiasFieldCorrected._regAtlasnrrd:
T2 or PD image registered to the T1registered image
4.2.4AutoSeg/ems{_N}
-ABCparam.xml:
- EMS{_N}.log:
XML parameter file
Log file
-EMS{_N}.xml :
XML file
- {T1,T2,PD}Image_regAtlas_corrected_EMS{_N}.nrrd
Images filtered and intensity inhomogeneity
corrected
-T1Image_regAtlas_labels_EMS{_N}.nrrd :
Label Image (WM+GM+CSF)
-T1Image_regAtlas_posterior0_EMS{_N}.nrrd :
White matter probabilistic map
-T1Image_regAtlas_posterior1_EMS{_N}.nrrd :
Grey matter probabilistic map
-T1Image_regAtlas_posterior2_EMS{_N}.nrrd :
CSF probabilistic map
-T1Image_regAtlas_template_affine_EMS{_N}.nrrd: Template registered image
-{T1,T2,PD}Image_regAtlas_registered_EMS{_N}.nrrd:
Registered images
-T1Image_regAtlas_to_template_EMS{_N}.affine:
Transformation files
-{T1,T2}Image_regAtlas_to_{T1,T2}Image_regAtlas_registered_EMS{_N}.affine:
Transformation files
If 'Compute volume' option is selected:
-T1Image_regAtlas_WM.nrrd:
White matter label map
-T1Image_regAtlas_GM.nrrd:
Grey matter label map
-T1Image_regAtlas_CSF.nrrd:
CSF label map
-T1Image_regAtlas_WM_vol.txt:
White matter volume information text file
-T1Image_regAtlas_GM_vol.txt:
Grey matter volume information text file
-T1Image_regAtlas_CSF_vol.txt:
CSF volume information text file
4.2.5AutoSeg/Stripped
-T1Image_labels_EMS_mask.nrrd
Binary Mask (WM+GM+CSF)
-{T1,T2,PD}Image_corrected_EMS-stripped.nrrd
Skull Stripped image
-T1Image_corrected_EMS-stripped-irescaled.nrrd
Intensity rescaled skull-stripped image
4.2.6AutoSeg/WarpROI

Affine registration step
- AtlasAffReg-T1Image_regAtlas_corrected_EMS-stripped-irescaled_initializetransform.txt
Log file
- AtlasAffReg-T1Image_regAtlas_corrected_EMS-stripped-irescaled.nrrd
Atlas image affinely registered to the skull-stripped intensity rescaled image

Deformable registration step
- AtlasWarpReg-T1Image_regAtlas_corrected_EMS-stripped-irescaled_transformation.nrrd
Deformation field
- AtlasWarpReg-T1Image_regAtlas_corrected_EMS-stripped-irescaled.nrrd
Atlas image warped to the skull-stripped intensity rescaled image

Applying the transformations

Subcortical structures
10 probabilistic maps have been defined in our atlas: amygdalaLeft, amygdalaRight, caudateLeft, caudateRight,
hippocampusLeft, hippocampusRight, pallidusLeft, pallidusRight, putamenLeft and putamenRight.
For each structure:
- T1Image_regAtlas_corrected_EMS--${Structure}-AffReg.nrrd
Probability map affinely registered to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS--${Structure}-WarpReg.nrrd
Probability map warped to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS--${Structure}-WarpReg-HardSeg.nrrd
Binary image (hard segmentation of the probabilistic map)
If the 'Compute volume' option has been selected:
- T1Image_regAtlas_corrected_EMS--${Structure}-WarpReg_vol.txt
Volume information text file

Lateral ventricles
For each lateral ventricle:
- T1Image_regAtlas_corrected_EMS—latVentricleMask-AffReg-BinMask.nrrd
Binary mask affinely registered to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS—latVentricleMask-WarpReg-BinMask.nrrd
Binary mask warped to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS—latVentricleMask-WarpReg.nrrd
Probabilistic map, obtained by masking the CSF probabilistic map with the binary mask
- T1Image_regAtlas_corrected_EMS—latVentricleMask-WarpReg-HardSeg.nrrd
Binary image (hard segmentation of the probabilistic map)
If the 'Compute volume' option has been selected:
- T1Image_regAtlas_corrected_EMS—latVentricleMask-WarpReg-HardSeg_vol.txt
Volume information text file

Generic ROI maps
- T1Image_regAtlas_corrected_EMS--GenericROIMap-AffReg.nrrd
Generic ROI Map affinely registered to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS--GenericROIMap-WarpReg.nrrd
Binary image (hard segmentation of the probabilistic map)
If the 'Compute volume' option has been selected:
- T1Image_regAtlas_corrected_EMS—GenericROIMap-WarpReg_volumeSummary.csv
Volume information file

Parcellation maps
- T1Image_regAtlas_corrected_EMS--ParcellationMap-AffReg.nrrd
Generic ROI Map affinely registered to the skull-stripped intensity rescaled image
- T1Image_regAtlas_corrected_EMS--ParcellationMap-WarpReg.nrrd
Binary image (hard segmentation of the probabilistic map)
If the 'Compute volume' option has been selected:
- T1Image_regAtlas_corrected_EMS--ParcellationMap-WarpReg_volumeSummary.csv
Volume information file

ROI gathering
For all the subcortical structures:
- AtlasWarpReg-T1Image_regAtlas_corrected_EMS-stripped-irescaled-AllROI.nrrd
Label map gathering all the regions of interest
4.2.7AutoSeg/MRMLScene
-Image_MRMLScene/Image_MRMLScene.mrml
MRML Scene
-Image_MRMLScene/Model_N.vtk
Nth model
5Screenshots
6Credits
. Sylvain Gouttard for the original pipeline
References:

Gouttard S, Styner M, Joshi S, Smith RG, Hazlett HC, Gerig G, Subcortical Structure Segmentation using
Probabilistic Atlas Priors, Medical Image Computing and Computer Assisted Intervention, 3D Segmentation
in the Clinic: A grand Challenge, October 2007, p 37-46.
. Kitware Inc. : http://www.batchmake.org
BatchMake is a crossplatform tool for batch processing of large amount of data. It can process datasets
locally or on distributive systems using Condor.
. Condor: http://www.cs.wisc.edu/condor/
Condor allows users to run High Throughput Computing on large collections of distributively owned
computing resources.
. RView package (Daniel Rueckert): http://www.doc.ic.ac.uk/~dr/software/
The Image Registration Toolkit was used under Licence from Ixico Ltd.
References:
 D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes. Non- rigid registration
using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging,
18(8):712-721, 1999.
 J. A. Schnabel, D. Rueckert, M. Quist, J. M. Blackall, A. D. Castellano Smith, T. Hartkens, G. P. Penney,
W. A. Hall, H. Liu, C. L. Truwit, F. A. Gerritsen, D. L. G. Hill, and D. J. Hawkes. A generic framework for
non-rigid registration based on non-uniform multi-level free-form deformations. In Fourth Int. Conf. on
Medical Image Computing and Computer- Assisted Intervention (MICCAI '01), pages 573-581, Utrecht,
NL, October 2001.
 C. Studholme, D.L.G.Hill, D.J. Hawkes, An Overlap Invariant Entropy Measure of 3D Medical Image
Alignment, Pattern Recognition, Vol. 32(1), Jan 1999, pp 71-86.
. itkEMS (Marcel Prastawa): http://www.ia.unc.edu/dev/download/itkems/index.hm
References for the original method:
 Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model based tissue classification of MR
images of the brain. IEEE TMI 1999;18:897–908.
 Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model based bias field correction of MR
images of the brain. IEEE TMI 1999;18:885–896.
. ABC (Marcel Prastawa): Atlas Based Classification - http://www.nitrc.org/projects/abc/
ABC (Atlas Based Classification) is a full segmentation pipeline developed and used at University of North
Carolina and University of Utah for brain MRIs. The processing pipeline includes image registration, filtering,
and inhomogeneity correction. The tool is cross-platform and can be run within 3D Slicer or as a stand-alone
program.
This software is based on itkEMS.
. WarpTool, fWarp, txApply: Sarang Joshi, Brad Davis
References:
 Sarang Joshi, Brad Davis, Matthieu Jomier, and Guido Gerig, "Unbiased Diffeomorphic Atlas Construction
for Computational Anatomy," NeuroImage; Supplement issue on Mathematics in Brain Imaging, (PM
Thompson, MI Miller, T Ratnanather, RA Poldrack, and TE Nichols, eds.), vol. 23, no. Supplement1, pp.
S151-S160, Elsevier, Inc, 2004