MIRACL in a nutshell¶
MIRACL (Multi-modal Image Registration And Connectivity anaLysis) is a general-purpose, open-source pipeline for automated:
Registration of mice CLARITY data to the Allen Reference Atlas
Segmentation and feature extraction of mice CLARITY data in 3D (Sparse and nuclear staining)
Registration of mice multimodal imaging data (MRI & CT, in-vivo & ex-vivo) to Allen Reference Atlas
Tract or label specific connectivity analysis based on the Allen Connectivity Atlas
Comparison of diffusion tensort imaging (DTI)/tractography, virus tracing using CLARITY and Allen Connectivity Atlas
Statistical analysis of CLARITY and imaging data
Atlas generation and Label manipulation
Program structure¶
MIRACL is structured into Modules and Workflows.
Modules¶
The pipeline is comprised of different Modules
depending on their
respective functionality. Functions for each module are grouped together:
Module |
Functionality |
---|---|
connect |
Connectivity |
Conversion (Input/Output) |
|
Registration |
|
seg |
Segmentation |
lbls |
Labels |
Utilities |
|
sta |
Structure Tensor Analysis |
stats |
Statistics |
An example of using a module would be to run the clar_allen
function which
performs a CLARITY whole-brain registration to Allen Atlas on a nifti
image (down-sampled by a factor of five):
$ miracl reg clar_allen -i niftis/SHIELD_05x_down_autoflor_chan.nii.gz -o ARI -m combined -b 1
The above command uses the -i
flag to select the nifti file, -o
to
specify the orientation of the image, -m
to register to both hemispheres
and -b
to include the olfactory bulb.
Workflows¶
The workflow (flow) module combines multiple functions from the above modules for ease of use to perform a desired task.
For example, a standard reg/seg analysis could look like this:
First perform registration of whole-brain CLARITY data to ARA:
$ miracl flow reg_clar -h
Then perform segmentation and feature extraction of full resolution CLARITY data:
$ miracl flow seg -h
Or structure tensor analysis:
$ miracl flow sta -h