Transcript Python Stochastic Tractography Module

Slicer3 Training Compendium
Slicer3 Training Tutorial
Python Stochastic Tractography Module
Julien von Siebenthal, Sylvain Bouix, Marek Kubicki, (RA Andrew Rausch)
Psychiatry Neuroimaging Lab
Pujol S, Gollub R
National Alliance for Medical Image Computing
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Introduction
The python stochastic tractography module contains the
tools necessary to extract, visualize and quantify white matter
connections from DTI images. It seeds nerve fiber bundles
from regions of interest (ROIs) based on DWI images. Unlike
streamline tractography, stochastic tractography uses a
probabilistic framework to perform tractography.
By incorporating uncertainty due to fiber crossings, imaging
noise and resolution, stochastic tractography can robustly
extract fiber bundles when streamline tractography cannot.
The tracts generated by the stochastic tractography can be
used to generate a connectivity probability image, which can
be used to study connectivity between different regions of the
brain.
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Materials and Req.’s
This course requires the installation of the Slicer3 software
and training dataset accessible at the following locations:
•
Slicer 3 Software (Slicer 3.5 nightly build as of January 3rd, 2010)
–
•
Training Dataset (packaged and compressed)
–
•
http://www.na-mic.org/Wiki/images/0/01/IJdata.tar.gz
Training Wiki
–
•
http://slicer.org/pages/Special:SlicerDownloads
http://www.na-mic.org/Wiki/index.php/Python_Stochastic_Tractography_Tutorial
Prerequisite Skills
– Loading images into Slicer 3
Disclaimer
It is the responsibility of the user of 3D Slicer to
comply with both the terms of the license and with
the applicable laws, regulations and rules.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Data
This course is built upon three datasets from a
single healthy subject brain:
DWI (Nrrd)
Optional:
Whitematter
Image (Nrrd)
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
ROI Image (Nrrd)
shown overlaid on
baseline DWI, tan
pixel is seed ROI
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ROI Image
Regions of Interest (ROI) are
denoted by an integer label.
An ROI image overlaid on FA image, regions with zero label are transparent.
The ROI image is an integer image. Each value defines a unique region.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Learning objective
Following this tutorial,
you’ll be able to seed
nerve fiber bundles
and compute a
connectivity map
using the stochastic
tractography module.
This map can be used
for further analysis.
INPUT:
DWI,
ROI Images
Python Stochastic
Tractography Module
OUTPUT:
Connectivity
Map
Further Analysis
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Enabling Daemons
• Also note, if this is your first time using a python module, you
must change one slicer setting.
– On the top menu, click “View”
– Go down to “Application Settings” and select it.
– Click the “Slicer Settings” Tab
– Click the box to “Slicer Daemon” to enable Slicer
Daemons.
– You must now close and restart Slicer 3 for these changes to
take effect.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Loading Volumes
Click the ‘Load Data’
folder button in the
upper left part of the
program. On the
dropdown menu that
appears, choose
‘Add data’
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Loading images
• In the ‘Add Data’ box that
appears, Click Add File(s)
• Search file directory for your
dwi nrrd file. In this tutorial,
we will be using namic01dwi_compressed.nhdr
• Click Open
• Click Add File(s) again
• Search your file directory for
ROI file(s). We will use
namic01-ROIcorpuscallosum.nhdr
• Click Open
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Loading ROI
• For each image you’re loading,
select the ‘Centered’ box
• For your ROI, select the
‘LabelMap’ box
• If you’re using multiple ROIs,
you should load those now the
same way, by clicking Add
File(s), opening the appropriate
nrrd, and checking the Centered
and LabelMap options – this is
optional, and we won’t do so at
this point in the tutorial
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Different Data Sets
• This module was created on the framework that
it could be used by anyone with a diffusion
weighted image without external preprocessing
steps, therefore making it available to anyone
interested in probabilistic tractography. As long
as the data can be loaded into Slicer, it can
undergo Python Stochastic Tractography.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Verifying the ROI
• 1st Click the Rings
to link views.
• Set B(ackground)
as DWI.nhdr image
• Set F(oreground) as
ROI.nhdr image
• Use slider to
examine ROI and
make sure it is over
correct area of DWI.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Verifying the ROI
• Slide left and right
to examine the
position of your ROI
in the 3D space of
the brain.
• In our case, the tan
dot is placed
centrally in the
corpus callosum,
just as we expect.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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White Matter Mask
• A white matter mask is used
to include only white matter
portions of the brain in
tractography to save
computation time.
• See an example WM Mask
by loading namic01-dwicompressed.nhdr from the
tutorial dataset.
• If you don’t have a mask for
your DWI, the next steps will
give us the upper and lower
thresholds needed by the
Stochastic Tractography
module to automatically
create one.
White matter mask overlaid on DWI baseline image
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Extract DWI
• Click the Modules drop
down tab.
• Select Diffusion
• Select Diffusion Tensor
Estimation
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Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Extract DWI
• Click the box next to Input
DWI Volume and select
namic01-dwicompressed.nhdr
• Click the boxes next to
Output DTI Volume, Output
Baseline Volume, and Otsu
Threshold Mask, and for
each option select Create
New Volume
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Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Selecting the Baseline
• Once this has
completed, we will
load the baseline for
finding our white
matter mask
thresholds
• Set B(ackground)
as baseline image,
which has lowest
number of all the
images created.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Thresholding Baseline
• Click the Modules drop down tab and
select Volumes.
• Set the Active Volume to the new
Baseline Image.
• Adjust the Window/Level values by moving
the sliders to make the grey scale image
easily viewable
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Baseline Intensities
• Bright white areas of the
ventricles should be
excluded
• Dark areas outside the
brain should also be
excluded.
• Moving your mouse over
the image, find the range
of numbers you want to
include inside the brain
by looking at the ‘Bg’
value shown in the corner
of your active axis
• Record the upper and
lower thresholds for later
use.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Launch Stochastic Tractography
• Click the Modules drop
down tab.
• Select Diffusion.
• Select Python Stochastic
Tractography.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Input/Output Tab
• Click Input DWI Volume
drop-down menu and select
the namic01-dwicompressed.nhdr
• Click Input ROI Volume
drop-down menu and select
the namic01-ROIcorpuscallosum.nhdr
• Optional:
• Enter in a second ROI (for
filtering) or White Matter
Volume
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Input/Output Tab
• If you would like not only all
the tracts coming from one
seed ROI, but rather all the
tracts coming from one seed
ROI that also go though a
second ROI, you should
select that ROI as your Input
ROI Volume (Region B)
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Input/Output Tab
• If you would prefer to use
your own previously
generated white matter
mask, select Input WM
Volume and select your
mask.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Smoothing Tab
• To enable Gaussian
Smoothing, click the box
next to Enabled so the
check mark is displayed
• Enter in FWHM (Full width
at half max in millimeters)
numbers for each direction.
This is the ‘spread’ of the
gaussian in the x, y, and z
directions. The larger the
value the more blurring will
occur.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Brain Mask Tab
• To enable the mask, click
the box next to Enabled so
the check mark is displayed
• Enter in Lower and Higher
brain threshold numbers
(based on baseline intensity
values). We will use 200800.
• These are the values you
determined by looking at the
‘Bg’ values on the extracted
baseline.
• A wider threshold will
include more of the brain,
but may take more
processing time
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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IJK/RAS switch
• Leave this box checked if
your scans are in IJK space.
We will check the box for the
tutorial.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Diffusion Tensor Tab
• Check Enabled as well as
FA, TRACE, and/or MODE
to output anisotropy index
images.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Tractography Tab
• Set Total Tracts to the number on
tracts you want to be seeded from
each ROI voxel. Hundreds are needed
to ensure a reliable study. For a quick
and dirty tutorial example we’ll use 50.
• Set Maximum Tract Length to the
length (in mm). For the tutorial, we use
200mm so that no tracts are longer
than the length of the brain.
• Set Step Size as the distance between
each re-estimation of tensors (usually
between 0.5 and 1 mm). If this is larger
than your voxel size, you can possibly
jump over whole slices. In the tutorial,
we’ll use 0.8mm.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
• Check Stopping criteria and enter FA
value from 0 to 1 to terminate tracts
(e.g. to stop fibers from traveling
through the CSF). Normally this is not
used in stochastic tractography, and
we will not check it in this tutorial.
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Connectivity Map Tab
• Choose a Computation Mode.
• binary (each voxel is counted only
once if a fiber passes through it)
• cumulative (tracts are summed by
voxel independently) We’ll use this
setting for the tutorial.
• weighted (tracts are summed by
voxel depending on their length).
• Length Based only shows tracts meeting
length criteria. It is not used in this tutorial.
• small shows only the shortest 1/3rd of
tracts
• medium shows only the middle1/3rd
• large shows only the longest 1/3rd
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Connectivity Map Tab
• The remaining options only apply to 2-ROI
experiments, and so will not be used in
the tutorial.
• Threshold between 0 and 1 will cut
out tracts that have a lower fraction of
tracts on the connectivity map
• Tract Offset defines how far back
along a tract after its termination the
algorithm will search for membership
in a second ROI
• Use spherical ROI vicinity takes a
second ROI and expands it
spherically. Useful for very small
target ROIs. Vicinity sets the size of
the spherical expansion.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Automatic Server Initialisation
• In Linux and Mac only, this allows the
server running the algorithm to
automatically be started
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Running Tractography
• Now that settings are properly
configured, hit Apply to begin
tractography.
• If a pop-up appears asking to allow
external connections from 127.0.0.1,
click Yes.
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Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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…wait.
• Wait until computation is completed.
Completed!
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Module Outputs
• Once tractography is complete, it
should output 6 new images
• brain_### is the mask created by
the module
• tensor_### is the tensor image
• fa_### is an fa map
• trace_### is a trace map
• mode_### is a mode map
• cmFA_### is a normalized
connectivity map, where each
voxel has a value from 0 to 1
describing the percentage of the
total number of tracts that go
through this voxel
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Connectivity Map
• The connectivity map is the key output of stochastic tractography,
defining a probability of a tract coming from your seed ROI being
present at a given point. The image output of stochastic is a grey
scale image with values from 0 to 1 describing this probability.
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Threshold Indication
• Scroll over the
connectivity
map to see its
intensity values
under “Bg”. You
can threshold
the connectivity
map in the
Editor, or
examine it in 3D
with the Volume
Rendering
Module.
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Saving Outputs
• Go to File at the
top right corner
of the screen and
select Save.
• Rename the
newly created
files as you
choose and save
them in preferred
directory.
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Visualizations
• Connectivity files have now been created and can be visualized in
2D or 3D with a couple modules in Slicer.
• Editor
• VolumeRendering
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Threshold Label Maps
• Go to the Editor from the
Modules dropdown menu
• Next to Create Label Map
From select cmFA_###
• Select the Threshold tool
• Change the lower bound to
0.1 so that only voxels with
a 10% chance of
connecting or higher will
be included
• Press Apply
• This label map can be
used for label statistics
analysis of FA, Mode, etc.
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Volume Rendering Tab
• VolumeRendering will allow you to create
a 3D model of your probability cloud.
• Drop down the Modules tab and select
Volume Rendering.
• Under Background Volume, next to
Source, select cmFA_###. This will create
a probability cloud of the tracts out by
stochastic tractography.
• If you would like to threshold this cloud to
only contain connections with >10%
chance of connection, under the
Rendering tab, select the Volume Property
pane, check the Use Thresholding box to
enable, and set the lower value to 0.1.
Pujol S, Gollub
R
Rausch,
von Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Acknowledgements
National Alliance for Medical Image Computing
NIH U54 EB005149
Morphometry Biomedical Informatics Research Network
NIH U24 RRO21382
Surgical Planning Laboratory (BWH)
Psychiatry Neuroimaging Laboratory
Marek Kubicki’s NIH Grant
Pujol S,
Gollub
R
Rausch,
Terry,
von
von
Siebenthal,
Siebenthal,
Bouix,
Bouix,
Kubicki
Kubicki
National Alliance for Medical Image Computing
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Appendix
• The following images have been generated from either
Model Maker or Volume Rendering and modified
under Models and Volumes just to show you the many
things you can do with your Python Stochastic
Tractography Output.
• The brain based models are seeded from a corpus
callosum ROI from the data of Anna Rotarska-Jagiela
at Goethe-Universität.
• The helix based models are seeded from an arbitrary
ROI from the phantom of Sonia Pujol at BWH.
Pujol S,
Gollub
R
Terry,
von
Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Visualize Model
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Gollub
R
Terry,
von
Siebenthal,
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National Alliance for Medical Image Computing
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R
Terry,
von
Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Glyphs Turned on under Volumes
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Gollub
R
Terry,
von
Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Visualize glyphs with models
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Gollub
R
Terry,
von
Siebenthal,
Bouix, Kubicki
National Alliance for Medical Image Computing
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Thresholded
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Pujol S,
Gollub
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Terry,
von
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Bouix, Kubicki
National Alliance for Medical Image Computing
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Visualize Tensor (FA) Connectivity
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Gollub
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Terry,
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National Alliance for Medical Image Computing
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Pujol S,
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Terry,
von
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National Alliance for Medical Image Computing
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From Helix Phantom with ROI
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Terry,
von
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