patch_segmentation_pipeline.pl

RASCAL patch-based label fusion segmentation pipeline

patch_segmentation_pipeline.pl <input_t1.mnc> <output_prefix> --model-dir <dir> [options]

DESCRIPTION

patch_segmentation_pipeline.pl runs the RASCAL (Rapid Automatic Segmentation of the human Cerebral cortex using an Atlas Library) patch-based label fusion segmentation pipeline as described by Weier et al. (2014). The pipeline registers an input T1-weighted MRI volume to a set of pre-labelled atlas templates, then uses non-local patch-based label fusion to propagate anatomical labels from the atlas library to the subject.

The pipeline supports multiple registration backends including minctracc and ANTs. Preprocessing steps such as non-uniformity correction (N3/N4) and intensity normalization (NUYL) can be enabled. Graph-cut optimization (GCO) can be applied for spatial regularization of the segmentation. The model directory, which contains the atlas library, must be specified.

OPTIONS

--verbose: Print progress information during processing.
--qc: Generate quality control images.
--clobber: Overwrite existing output files.
--cleanup: Remove temporary files after processing.
--model-dir <dir>: Path to the model directory containing the atlas library. This option is required.
--subject <id>: Specify a subject identifier string for output naming.
--search <n>: Search radius in voxels for the patch-based label fusion. Default: 2.
--patch <n>: Patch radius in voxels for the patch-based label fusion. Default: 1.
--exclude <id>: Exclude a specific atlas subject from the library (e.g. for leave-one-out validation).
--classes <n>: Number of label classes in the atlas library. Default: 36.
--variant <name>: Name of the label variant to use from the model directory. Default: labels.
--threshold <f>: Confidence threshold for label assignment.
--compare <file>: Provide a manual segmentation for comparison and overlap statistics.
--short: Store output labels in short integer format.
--preprocess: Run preprocessing steps (intensity normalization, brain masking) before segmentation.
--minctracc: Use minctracc for nonlinear registration to the atlas templates.
--ants: Use ANTs for nonlinear registration to the atlas templates.
--build: Build the atlas library from the model directory.
--symmetric: Use symmetric registration.
--resample_labels_order <n>: Interpolation order for resampling label volumes. Default: 1.
--preselect: Preselect atlas subjects most similar to the input before label fusion.
--pairwise: Use pairwise label fusion strategy.
--refine: Refine the segmentation with additional iterations.
--qc_lut <file>: Specify a colour lookup table for quality control visualisation.
--nuc: Apply N3 non-uniformity correction during preprocessing.
--nuc3T: Apply non-uniformity correction optimised for 3T MRI scanners.
--no_nuyl: Disable NUYL intensity normalization.
--no_gco: Disable graph-cut optimization for label regularization.
--no_nl: Disable nonlinear registration.
--baa: Use BEaST-based brain mask for the analysis.
--list <file>: Specify the atlas sample list file within the model directory. Default: samples.lst.
--nomask: Do not apply a brain mask during processing.
--patch_normalize: Normalize patch intensities before comparison.

EXAMPLES

Run segmentation with the required model directory:

patch_segmentation_pipeline.pl subject_t1.mnc output_prefix --model-dir /path/to/atlas

Run with ANTs registration, QC output, and cleanup:

patch_segmentation_pipeline.pl subject_t1.mnc output_prefix \
    --model-dir /path/to/atlas --ants --qc --cleanup --verbose

Run with custom patch parameters and compare to manual labels:

patch_segmentation_pipeline.pl subject_t1.mnc output_prefix \
    --model-dir /path/to/atlas --search 3 --patch 2 \
    --compare manual_labels.mnc --clobber

AUTHOR

Vladimir S. Fonov. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University.

COPYRIGHTS