When using the MICe-build-model.pl registration software, after the rigid body (-lsq6) and affine (-lsq12) registrations, it is possible to perform non linear registrations (-nlin). This can be done using one of two registration packages: ANTS (specify: -nlin-registration-method mincANTS, article: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2276735/) and ANIMAL (specify: -nlin-registration-method minctracc, article: http://onlinelibrary.wiley.com/doi/10.1002/hbm.460030304/abstract). Below you will find information about how to specify parameters for these two non linear registration methods.
Advanced Normalization ToolS (mincANTS)
By default 3 non linear stages are run with parameters being set which work well for MRI mouse brain data with a resolution of 56 microns. Using the -nlin-protocol flag, you can specify a file which overrides the default setting. The following parameters can be specified (these are all to set flag for mincANTS):
option | required | purpose | example values |
|---|---|---|---|
memory | no, default=2 | specify the amount of GB of memory required for the stage | 2,3,4,... |
| time | no, default=120 | specify the maximum amount of minutes this stage will run for (used only when running on the SciNet cluster, or when your scheduling system requires you to provide a timeframe for your jobs | 30,60, 360,... |
use_mask | no, default=1 | specify whether or not to use a mask to limit the region of interest for the registration | 0 or 1 |
generation | yes | indicates which non linear stage this is | 1,2,3,4,... |
similarity_model | yes | specify a semicolon separated list of similarity models for mincANTS (see mincANTS -help). Each similarity model needs: 1) method (CC,MI,PR,MSQ) | "CC,1,3,none" "CC,1,3,none;CC,1,3,0.056_dxyz" "MI,1,3,none;CC,1,3,0.140_dxyz" |
transformation_model | yes | specify the transformation model. Takes two parts: 1) Model (Diff,Elast,Exp,SyN) | "SyN,0.4" "Elast,0.3" |
regularization | yes | specify the parameters of th regularization: 1) method (Gauss,DMFFD) | "Gauss,5,1" "Gauss,2,1" |
iterations | yes | specify the number of iterations for non linear optimization | 20, 8, 6,... |
Full default mincANTS protocol (for 56 micron data)
(
{memory => 1.75,
time => 60*4,
use_mask => 0,
generation => 1,
similarity_model => "CC,1,3,none;CC,1,3,0.056_dxyz",
transformation_model => "SyN,0.5",
regularization => "Gauss,5,1",
iterations => "100x100x100x0"},
{memory => 4.5,
time => 60*6,
use_mask => 1,
generation => 2,
similarity_model => "CC,1,3,none;CC,1,3,0.056_dxyz",
transformation_model => "SyN,0.4",
regularization => "Gauss,5,1",
iterations => "100x100x100x20"},
{memory => 4.5,
time => 60*8,
use_mask => 1,
generation => 3,
similarity_model => "CC,1,3,none;CC,1,3,0.056_dxyz",,
transformation_model => "SyN,0.4",
regularization => "Gauss,5,1",
iterations => "100x100x100x50"},
);
ANIMAL (minctracc)
By default 6 non linear stages are run with parameters being set which work well for MRI mouse brain data with a resolution of 30-60 microns. Using the -nlin-protocol flag, you can specify a file which overrides the default setting. The following parameters can be specified (these are all to set flag for minctracc):
option | required | purpose | example values |
|---|---|---|---|
generation | yes | indicates which non linear stage this is | 1,2,3,4,... |
iterations | yes | specify the number of iterations for non linear optimization | 20, 8, 6,... |
kernel | yes | specify the blurring kernel to be used for the target and source | 0.3, 0.24, 0.1,... |
memory | no, default=2 | specify the amount of GB of memory required for the stage | 2,3,4,... |
optimization | no, default="-use_simplex" | specify what optimization to use for the non linear stages | "-use_simplex" (more to come...) |
simplex | yes | specify the radius of the simplex volume | 5, 4, 3, 2,... |
step | yes | specify the step size along x,y and z | 0.7, 0.5, 0.12,... |
use_gradient | yes | specify whether or not to use the gradient of the blur as a second non linear stage | 0 or 1 |
w_rotation | no, default="0.0174533,0.0174533,0.0174533" | specify the optimization weight of rotations around x, y, z | (any three float numbers, seperated by commas and between quotes) |
w_scales | no, default="0.02,0.02,0.02" | specify the optimization weight of scaling along x, y, z | (any three float numbers, seperated by commas and between quotes) |
w_shear | no, default="0.02,0.02,0.02" | specify the optimization weight of shears a,b and c | (any three float numbers, seperated by commas and between quotes) |
w_translations | no, default="0.4,0.4,0.4" | specify the optimization weight of translations in x, y, z | (any three float numbers, seperated by commas and between quotes) |
Example and usage of the -nlin-protocol flag
In this example the standard 6 non linear stages are replaced by two stages only:
MICe-build-model.pl ... -nlin-protocol 2nlin-protocol.pl ...
Where the file 2nlin-protocol.pl contains the following:
# This is a comment, the entire protocol is enclosed by parentheses
# followed by a semicolon
(
# Nlin stages are seperated by a block of parameters
# between curly brackets a comma
{ memory => 2,
generation => 1,
kernel => 0.3,
iterations => 40,
w_translations => "1,1,1",
simplex => 20,
use_gradient => 1,
step => 0.4,
},
# The second nlin stage starts here. Note that
# not all the non required parameters have been
# specified, and that all key/value pairs are
# separated by a comma
{ memory => 4,
generation => 2,
kernel => 0.15,
iterations => 20,
simplex => 20,
use_gradient => 0,
w_shear => "0.03,0.03,0.03",
step => 0.2,
},
);
Full default minctracc protocol (for 60 micron data)
(
{ memory => 2,
generation => 1,
kernel => 0.3,
iterations => 20,
simplex => 5,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.7,
},
{ memory => 2,
generation => 2,
kernel => 0.2,
iterations => 6,
simplex => 2,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.6,
},
{ memory => 2,
generation => 3,
kernel => 0.2,
iterations => 8,
simplex => 2,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.5,
},
{ memory => 2,
generation => 4,
kernel => 0.2,
iterations => 8,
simplex => 2,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.24,
},
{ memory => 2,
generation => 5,
kernel => 0.1,
iterations => 8,
simplex => 2,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.12
},
{ memory => 5.25,
generation => 6,
kernel => 0.06,
iterations => 8,
simplex => 2,
ncpus => 1,
use_gradient => 1,
optimization => "-use_simplex",
step => 0.06
},
);