Launching a processing

Commands

The main file is located in workflows and is called segment_pnh.py and should be called like a python script:

$ python workflows/segment_pnh.py

N.B. if you have installed the pypi version (e.g. using pip install skullTo3d) or a docker/singularity version, you can replace the previous command by the following command:

$ segment_pnh

For container (docker and singularity), here are some examples - add your proper bindings:

$ docker run -B binding_to_host:binding_guest macatools/macapype:latest segment_pnh

$ singularity run -v binding_to_host:binding_guest /path/to/containers/macapype_v0.0.4.1.sif segment_pnh

Expected input data

All the data have to be in BIDS format to run properly (see BIDS specification for more details)

In particular:

  • _T1w (BIDS) extension is expected for T1 weighted images (BIDS)

  • _T2w (BIDS) extension is expected for T2 weighted images (BIDS)

_images/BIDS_orga.jpg

Note : All files with the same extension (T1w or T2w) will be aligned to the first one and averaged

Command line parameters

The following parameters are mandatory

  • -data

the path to your data dataset (existing BIDS format directory)

  • -out

the path to the output results (an existing path)

  • -soft

can be one of theseSPM or ANTS

  • with _robustreg (at the end) to have a more robust registration (in two steps)

  • with _test (at the end) to check if the full pipeline is coherent (will only generate the graph.dot and graph.png)

  • with _prep (at the end) will perform data preparation (no brain extraction and segmentation)

  • with _noseg (at the end) will perform data preparation and brain extraction (no segmentation)

  • with _seq (at the end) to run in sequential mode (all iterables will be processed one after the other; equivalent to -nprocs 1)

The following parameters are exclusive

(but one is mandatory)

  • -params

    (mandatory if -species is omitted)

a json file specifiying the global parameters of the analysis. See Parameters for more details

  • -species

    (mandatory if -params is omitted)

followed the NHP species corresponding to the image, e.g. {macaque | marmo | baboon | chimp} In extra, marmoT2 can be used for segmenting from the T2w image (by default, T1w is used)

The following parameters are optional

(but highly recommanded)

  • dt

specifies the datatype available to perform brain segmentation (can be “T1”, or “T1 T2”). Note : default is T1 if the attribute is omitted

  • -deriv

    creates a derivatives directory, with all important files, properly named following BIDS derivatives convertion

  • -pad

    exports (in derivatives) important files in native (original) space

The following parameters are optional

  • -indiv or -indiv_params : a json file overwriting the default parameters (both macapype default and parameters specified in -params json file) for specific subjects/sessions. See Individual Parameters for more details

  • -sub (-subjects), -ses (-sessions), -acq (-acquisions), -rec (-reconstructions) allows to specifiy a subset of the BIDS dataset respectively to a range of subjects, session, acquision types and reconstruction types. The arguments can be listed with space seperator. Note if not specified, the full BIDS dataset will be processed

  • -mask allows to specify a precomputed binary mask file (skipping brain extraction). The best usage of this option is: precomputing the pipeline till brain_extraction_pipe, modify by hand the mask and use the mask for segmentation. Better if only one subject*session is specified (one file is specified at a time…).

Warning: the mask should be in the same space as the data. And only works with -soft ANTS so far

  • -nprocsan integer, to specifiy the number of processes that should be allocated by the parralel engine of macapype

    • typically equals to the number of subjects*session (i.e. iterables).

    • can be multiplied by 2 if T1*T2 pipelines are run (the first steps at least will benefit from it)

    • default = 4 if unspecified ; if is put to 0, then the sequential processing is used (equivalent to -soft with _seq, see before)

Command line examples

$ python workflows/segment_pnh.py -data ~/Data_maca -out ./local_test -soft ANTS -params params.json

$ python workflows/segment_pnh.py -data ~/Data_maca -out ./local_test -soft ANTS_robustreg -species macaque

$ python workflows/segment_pnh.py -data ~/Data_maca -out ./local_test -soft ANTS -params params.json -sub Apache Baron -ses 01 -rec mean -deriv -pad