.. _scil_tractogram_segment_with_recobundles:

scil_tractogram_segment_with_recobundles
========================================

::

	usage: __main__.py [-h]
	                   [--tractogram_clustering_thr TRACTOGRAM_CLUSTERING_THR]
	                   [--model_clustering_thr MODEL_CLUSTERING_THR]
	                   [--pruning_thr PRUNING_THR] [--slr_threads SLR_THREADS]
	                   [--seed SEED] [--inverse] [--no_empty]
	                   [--in_pickle IN_PICKLE | --out_pickle OUT_PICKLE]
	                   [--in_tractogram_ref IN_TRACTOGRAM_REF]
	                   [--in_model_ref IN_MODEL_REF]
	                   [-v [{DEBUG,INFO,WARNING,ERROR}]] [-f]
	                   in_tractogram in_model in_transfo out_tractogram
	
	Segment a single bundle by computing a simple Recobundles (single-atlas &
	single-parameters).
	
	For multiple bundles segmentation (using RecobundlesX / BundleSeg), see instead
	>>> scil_tractogram_segment_with_bundleseg
	
	Hints:
	- The model needs to be cleaned and lightweight.
	- The transform should come from ANTs: (using the --inverse flag)
	  >>> AntsRegistrationSyNQuick.sh -d 3 -m MODEL_REF -f SUBJ_REF
	  If you are unsure about the transformation 'direction', try with and without
	  the --inverse flag. If you are not using the right transformation 'direction'
	  a warning will pop up. If there is no warning in both cases, it means the
	  transformation is very close to identity and both 'directions' will work.
	
	-------------------------------------------------------------------------------
	Reference:
	[1] Garyfallidis, E., Cote, M. A., Rheault, F., ... & Descoteaux, M. (2018).
	    Recognition of white matter bundles using local and global streamline-based
	    registration and clustering. NeuroImage, 170, 283-295.
	-------------------------------------------------------------------------------
	
	positional arguments:
	  in_tractogram         Input tractogram filename.
	  in_model              Model bundle to use for recognition. (Ex, a .trk file.
	  in_transfo            Path for the transformation to model space (.txt, .npy or .mat).
	  out_tractogram        Output tractogram filename.
	
	options:
	  -h, --help            show this help message and exit
	  --seed SEED           Random number generator seed [None].
	  --inverse             Use the inverse transformation.
	  --no_empty            Do not write file if there is no streamline.
	  --in_pickle IN_PICKLE
	                        Input pickle clusters map file.
	                        Will override the tractogram_clustering_thr parameter.
	  --out_pickle OUT_PICKLE
	                        Output pickle clusters map file.
	  --in_tractogram_ref IN_TRACTOGRAM_REF
	                        Reference anatomy for in_tractogram (if tck/vtk/fib/dpy) file
	                        support (.nii or .nii.gz).
	  --in_model_ref IN_MODEL_REF
	                        Reference anatomy for in_model (if tck/vtk/fib/dpy) file
	                        support (.nii or .nii.gz).
	  -v [{DEBUG,INFO,WARNING,ERROR}]
	                        Produces verbose output depending on the provided level. 
	                        Default level is warning, default when using -v is info.
	  -f                    Force overwriting of the output files.
	
	Recobundles options:
	  --tractogram_clustering_thr TRACTOGRAM_CLUSTERING_THR
	                        Clustering threshold used for the whole brain. Default: 8.
	  --model_clustering_thr MODEL_CLUSTERING_THR
	                        Clustering threshold used for the model [4mm].
	  --pruning_thr PRUNING_THR
	                        MDF threshold used for final streamlines selection [6mm].
	  --slr_threads SLR_THREADS
	                        Number of threads for SLR [1].
	
	2.2.2