Implementation of Multiple Automatically Generated Templates brain segmentation algorithm MAGeTbrain built upon antsRegistration and the qbatch generic cluster submission system.
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bash version 3.0 or later
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python version 2.7.x or later
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qbatch git version
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ANTs with ITK_BUILD_MINC_SUPPORT
MAGeTbrain is very computationally expensive, performing
atlases*templates + templates*subjects linear and non-linear registrations.
This produces large number of files of moderate file size. Typical subject
pools produce outputs on the scale of 100's of GB.
MAGeTbrain was developed to produce high-quality segmentations (labels) of anatomical areas in structural MRI volumes. It operates by the principle of starting with a small number of high-quality expertly segmented atlases and transforming them onto a subject pool through an intermediate registration to a "representative" subset of subjects called the template pool. Through pair-wise registration from atlas-template and template-subject the number of candidate segmentations is increased while the template pool absorbs the large-scale methodological and structural variation of the subject pool. After all candidates have been produced the resulting segmentations are fused via a majority vote scheme.
MAGeTbrain accepts any expertly segmented MRI volume/label pairs as atlas inputs (such as those available at http://cobralab.ca/atlases/ for the hippocampus, subcortical and cerebellar structures) or you may provide your own for other structures. An odd number of atlases is strongly recommended to avoid tie votes during the label fusion process.
Templates, which are a duplication of a subset of the subject pool should be chosen to span the anatomical variability of the subject pool. Typical template pool size is 21 subjects. Based on simulations of MAGeTbrain, additional subjects beyond 21 provides minimal improvement in the final outputs. The size of the template pool should again be an odd number to avoid voting ties. For subject pools smaller than 21, include all subjects also in the template pool while maintaining an odd number if need be. File names for a subject included in both the subject and template pool should be the same so that MAGeTbrain can skip registering identical images together.
Typical application of MAGeTbrain is to preprocess input MRI volumes prior to starting to correct for bias fields and to crop excess non-head features. One such recommended pipeline based on the MINC tools is available at https://github.com/CobraLab/minc-bpipe-library. Subject input volumes should be corrected but otherwise in native (or otherwise non volumetrically deformed) space, in order to ensure label volumes provide real-world measures. Subject scans must all be oriented in the same manner for affine registration to succeed. MAGeTbrain was tested and optimized on 1x1x1 mm isotropic MPRAGE subject data. It has been very successfully used on higher resolution data and on other contrast types but may require tweaking of time/memory estimates.
> git clone https://github.com/CobraLab/antsRegistration-MAGeT.git
> module load cobralab/2019b
> source antsRegistration-MAGeT/bin/activate
> cd /path/to/my/working/Directory
> mb.sh -- init
2016-06-21 20:46:49 UTC [ info] Creating input/atlas input/template input/subject
2016-06-21 20:46:49 UTC [ info] Cleaning up. Done
# Copy atlas/template/subject files into input directories
> mb.sh -- run
2016-06-21 14:32:53 UTC [ info] Found:
2016-06-21 14:32:53 UTC [ info] 5 atlases in input/atlas
2016-06-21 14:32:53 UTC [ info] 1 labels per atlas in input/atlas
2016-06-21 14:32:53 UTC [ info] 13 templates in input/template
2016-06-21 14:32:53 UTC [ info] 13 subjects in input/subject
2016-06-21 14:32:53 UTC [ info] 0 models in input/models
2016-06-21 14:32:53 UTC [ info] Progress:
2016-06-21 14:32:53 UTC [ info] 0 of 65 atlas-template registrations completed
2016-06-21 14:32:53 UTC [ info] 0 of 156 template-subject registrations completed
2016-06-21 14:32:53 UTC [ info] 0 of 845 resample labels completed
2016-06-21 14:32:53 UTC [ info] 0 of 13 voted labels completed
2016-06-21 14:32:53 UTC [ info] Computing Atlas to Template Registrations
36648384[].gpc-sched-ib0
...
2016-06-21 14:32:55 UTC [ info] Computing Template to Subject Registrations
36648397[].gpc-sched-ib0
...
2016-06-21 14:32:58 UTC [ info] Computing Label Resamples
36648410.gpc-sched-ib0
...
2016-06-21 14:33:03 UTC [ info] Computing Votes
36648424.gpc-sched-ib0
...
2016-06-21 14:33:07 UTC [ info] Cleaning up. Done
# For options and individual stage control, see mb.sh --helpMAGeTBrain runs in a number of stages, some of which can run in parallel and
others which must run after prior stages are completed. In addition, this
pipeline describes a number of utility stages for assisting in running
MAGeTbrain. All of these stages can be run by invoking them after -- in
your mb.sh call.
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init- setup the input directory structure for MAGeTbrain -
status- display the status check counting work completed and work to be done and exit -
cleanup- create and submit a job to tar, compress and delete all intermediate files, for use after a successful run
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template- register atlases to templates -
subject- register templates to subjects -
resample- transform candidate label files through atlas-template-subject chain. Depends on completion oftemplateandsubjectstages -
vote- perform majority vote label fusion on candidate labels -
run- calculate and submit all standard stages
Stages manually specified on the command line do not check if their antecedent stages have completed successfully, this can result in undefined behavior. If you specify stages manually, please ensure that antecedent stages are complete.
Commands in a given stage in MAGeTbrain are deemed complete if their output files exist, this means that if a pipeline was stopped at some point, it can resume by examining the existing files. If input files are changed be careful to cleanup old intermediate files.
Multi-atlas mode in MAGeTbrain disables the "template" concept, resulting in operation like a classic multi-atlas segmentation tool. All subjects are ignored for this mode, instead templates are treated as subjects.
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multiatlas-resample- transform candidate label files through atlas-template chain, treating templates as if they were subjects. Depends upon completion oftemplatestage -
multiatlas-vote- perform majority vote label fusion on template candidate labels -
multiatlas- performtemplate,multiatlas-resampleandmultiatlas-votestages
Typical use of this mode is for verification vs MAGeTbrain mode and for manual
"best" template selection. For template selection, include all your subjects as
templates, run multiatlas mode, then QC the resulting labels. Choose the
bestquality labels from the template pool and use those subjects as your
templates. Run MAGeTbrain as normal from there.
MAGeTbrain was originally designed and optimized for the case of CoBrALab's 0.3 mm isotropic atlases and 1 mm isotropic template/subject whole-brain MRIs with T1 or T2 contrasts.
This new version of MAGeTbrain will attempt to compute walltime and memory requirements for a given input resolution based on some empirical research https://github.com/gdevenyi/antsRegistration-benchmarking plus a 15% safety factor for errors in estimates.
Currently needs reimplementation
Using a brain or ROI mask provides a number a number of potential benefits, improved registrations and reduction in memory requirements, although these benefits have not been throughly examined empirically.
Testing thus far has indicated masking is most effective if used to remove non-brain tissues or areas of interest, rather than focusing registrations. As such it is recommended to skull-strip input scans. Particular attention should be paid to cerebllar regions to not lose and cerebellum volume after cropping.
If enabled with the "label maksing" option -l, atlas to template registrations
will use the input labels to focus the registration, reducing runtime and
possibly improving registration quality, depending upon anatomical differences.
MAGeTbrain should successfully operate using slabs rather than whole brains as inputs. It may benefit from an ROI mask defining the boundary of the slab in order to prevent non-linear registrations from wasting cycles and avoiding possible sharp-edge effects on the smooth registration fields.
Subject populations with significant pathology or structural abnormalities pose particular problems in nonlinear registrations as structural correspondence is no longer guaranteed. In these cases, there are a few suggested methods to improve results. First, choose as templates subjects that have a "reasonable" level of pathology, rather than extreme examples. These templates can help to "bridge the gap" between atlases and subjects where large deformations are needed.
Secondly, one can attempt to find the "best" templates via application of the
multiatlas mode. These may in fact correspond with the same subjects as
suggested above.
Finally, special populations have been segmented with MAGeTbrain by (semi)manual segmentation of some subjects and their use as atlases. This has been particularly successful with neonates.
MAGeTbrain was designed and tested to run on Compute Canada's SciNet
supercomputing cluster located in Toronto, Canada. The cluster consists of
3000+ 8-CPU (16-core) compute nodes with 16 GB of RAM each. MAGeTbrain handles
job creation and submission via the qbatch job creation tool supporting
PBS, SGE and LSF(soon) clusters. qbatch is configured via a number of
environment variables, see https://github.com/pipitone/qbatch. qbatch
will split up MAGeTbrain jobs according to its configuration to honour
walltime and memory specifications.
To run MAGeTbrain locally, install qbatch and define
QBATCH_SYSTEM="local", qbatch will run commands locally using GNU
parallel. Note that MAGeTbrain running a single computer is a very slow
process. We estimate a processing time of approximately 350 hours for a 5 atlas
21 template, 1 subject run, and 60 hours per additional subject when running
on a single CPU. Processing is linearly decreased with more processors but
eventually memory limited on most desktop computers. We strongly recommend
installing MAGeTbrain on a cluster.
The following is an example data structure for a single atlas/template/subject.
The names atlas1, template1, and subject1 are arbitrary.
ext can be any image format ANTs/ITK supports, currently MINC2 (.mnc),
NIFTI1/2 (.nii or .nii.gz) and Analyze (.hdr and .img)
input/
atlas/
atlas1_t1.ext - mandatory MRI volume
[ atlas1_[t2, pd, fa, md].ext ] - co-registered to t1
atlas1_label_name.ext - mandatory label file, basename must match t1
[ atlas1_label_name2.ext ] - additional labels
[ atlas1_label_nameN.ext ] - arbitrary numbers of labels
[ atlas1_mask.ext ] - mask used to focus registration
### additional atlas/label pairs as desired
template/
subject1_t1.ext - filename should match subject with same MRI
[ subject1_[t2, pd, fa, md].ext ] - co-registered to t1, requires atlas to also have this contrast
[ subject1_mask.ext ] - mask used to focus registration
### additional templates as desired
subject/
subject1_t1.ext - filename should match subject with same MRI
[ subject1_[t2, pd, fa, md].ext ] - co-registered to t1, requires template to also have this contrast
[ subject1_mask.ext ] - mask used to focus registration
### additional subjects as desiredThe following describes the standard set of outputs for the input structure above
output/
transforms/
atlas-template/
template1_t1.ext/
atlas1_t1.ext-template1_t1.ext0_GenericAffine.xfm - MINC format affine transform
atlas1_t1.ext-template1_t1.ext1_NL.xfm -- MINC format nonlinear transform
atlas1_t1.ext-template1_t1.ext1_NL_grid_0.mnc -- MINC format nonlinear grid
### additional atlas to template1 registrations
### additional directories per template
template-subject/
subject1_t1.ext/
template1_t1.ext-subject1_t1.ext0_GenericAffine.xfm - MINC format affine transform
template1_t1.ext-subject1_t1.ext1_NL.xfm - MINC format nonlinear transform
template1_t1.ext-subject1_t1.ext1_NL_grid_0.mnc -- MINC format nonlinear grid
### additional template to subject1 registrations
### additional directories per subject
labels/
candidates/
subject1_t1.ext/
atlas1_t1.ext-template1_t1.ext-subject1_t1.ext-atlas1_label_name.ext - resampled candidate label
### additional candidate labels for each atlas-template-subject path
### additional candidate labels for each input label
majorityvote/
subject1_label_name.ext - final majority vote label
### additional labels for each input label
### additional labels for each subject
### Optional outputs for multiatlas mode
multiatlas/
labels/
candidates/
template1_t1/
atlas1_t1.ext-template1.ext-atlas1_label_name.ext - resampled candidate label
### additional labels for each atlas-template path
### additional directories for each template
majorityvote/
template1_label_name.ext - final majority vote label
### additional labels for each template
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