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prepare-data - Prepare input data for deep learning with PyTorch

This pipeline prepares images generated by Clinica to be used with the PyTorch deep learning library [Paszke et al., 2019]. Four types of tensors are proposed: 3D images, 3D patches, 3D regions or 2D slices.

Prerequisites

You will need to execute the Clinica pipeline corresponding to the modality argument before running this pipeline.

Running the pipeline

The pipeline can be run with the following command line: 

clinicadl prepare-data [image|patch|slice|roi] [OPTIONS] CAPS_DIRECTORY MODALITY

The command has four sub-commands:

  • image to convert the whole 3D image,
  • patch to extract 3D patches walking through the entire image,
  • roi to extract a list of regions defined by masks at the root in CAPS_DIRECTORY,
  • slice to extract 2D slices from the image.

which have the same arguments:

  • CAPS_DIRECTORY (Path) is the folder in a CAPS hierarchy containing the images corresponding to the MODALITY asked.
  • MODALITY (str) is the name of the preprocessing performed on the original images. It can be t1-linear, flair-linear or pet-linear. You can choose custom if you want to get a tensor from a custom filename.

prepare-data-from-bids- Extract tensors from a BIDS

You can also extract the tensor from BIDS directory using this pipeline. In this case, you don't need to execute the Clinica pipeline and so use unpreprocessed images with the PyTorch deep learning library.

Running the pipeline

The pipeline can be run with the following command line:

clinicadl prepare-data-from-bids [image|patch|slice|roi] [OPTIONS] BIDS_DIRECTORY CAPS_DIRECTORY MODALITY

The command has four sub-commands:

  • image to convert the whole 3D image,
  • patch to extract 3D patches walking through the entire image,
  • roi to extract a list of regions defined by masks at the root in CAPS_DIRECTORY,
  • slice to extract 2D slices from the image.

which have the same arguments:

  • BIDS_DIRECTORY (Path) is the folder in a BIDS hierarchy containing the images
  • CAPS_DIRECTORY (Path) is the folder in a CAPS hierarchy containing the output tensors.
  • MODALITY (str) is the type of the images. It can be t1, flair or pet. You can choose custom if you want to get a tensor from a custom filename.

options for both prepare-data and prepare-data-from-bids pipelines

Each sub-command has its own set of options. There are four generic options:

  • --subjects_sessions_tsv (Path) is a path to a TSV file listing participant and session IDs.
  • --extract_json (str) is the name of the JSON file that will be created to store all the information of the extraction step. Default will name the JSON file extract_{time_stamp}.json.
  • --n_proc (int) is the number of workers used to parallelize tensor extraction. Default: 2.

Default values

When using patch or slice extraction, default values were set according to [Wen et al., 2020].

Tip

Type clinicadl prepare-data [image|patch|slice|roi] --help to see the full list of parameters.

Outputs

Results are stored in following folder of the CAPS hierarchy: subjects/<participant_id>/<session_id>/deeplearning_prepare_data/<tensor_format>_based/<modality_folder>. <modality_folder> is equal to modality with all - replaced by a _.

Files are saved with the .pt extension and contains tensors in PyTorch format.

A JSON file is also stored in the CAPS hierarchy under the tensor_extraction folder: 

CAPS_DIRECTORY
└── tensor_extraction
        └── <extract_json>
These files are compulsory to run the train command. They provide all the details of the processing performed by the prepare-data command that will be necessary when reading the tensors.

Extraction method

In this section we consider the options needed and outputs produced for different extraction method commands. Each time we consider that the input is named <input_pattern>_<suffix>.nii.gz. The suffix represents the initial modality (it can be for example T1w).

Warning

The default behavior of the pipeline is to only extract images even if another extraction method is specified. However, all the options will be saved in the preprocessing JSON file and then the extraction is done when data is loaded during the training. If you want to save the extracted method tensors in the CAPS, you have to add the --save-features flag.

image

The image format saves all the input values. It does not require any option. The output filename is <input_pattern>_<suffix>.pt.

patch

The patch tensor format creates N patches which cover the whole image. Each patch is a 3D tensor of size patch_sizexpatch_sizexpatch_size. The center of the patches are separated by stride_size voxels. Then if stride_size < patch_size the patches will have some overlap, otherwise some voxels will not be seen.

Options:

  • --patch_size (int) patch size. Default value: 50.
  • --stride_size (int) stride size. Default value: 50.
  • --save_features (bool) Flag to specify if you want to save the patches as tensors in the CAPS. By default, the pipeline only extracts the images and specified patches are then extracted on-the-fly.

The output files are <input_pattern>_patchsize-<L>_stride-<S>_patch-<i>_<suffix>.pt: tensor version of the <i>-th 3D isotropic patch of size <L> with a stride of <S>.

roi

The roi format saves the regions defined by binary masks saved in the CAPS folder. Binary mask must be provided by the user, by using the option --roi_list.

Options:

  • --roi_list: list of N regions to be extracted. The masks corresponding to these regions should be written in <caps_directory>/masks/tpl-<tpl_name>. For example, if one wants to extract ROI corresponding to the right and left hippocampus using the publicly available MNI152NLin2009cSym template, two files containing the masks should be available in a folder named <caps_directory>/masks/tpl-MNI152NLin2009cSym/. For full (uncropped) images, the filenames of these masks are: tpl-MNI152NLin2009cSym_res-1x1x1_roi-leftHippocampusBox_mask.nii.gz, tpl-MNI152NLin2009cSym_res-1x1x1_roi-rightHippocampusBox_mask.nii.gz. Then, the command to invoque the extraction is: 

    clinicadl prepare-data CAPS_DIRECTORY t1-linear roi --roi_list rightHippocampusBox --roi_list leftHippocampusBox

  • --roi_uncrop_output: disables cropping option, so the output tensors have the same size as the whole image instead of the ROI size.

  • --roi_custom_template (mandatory for custom): only used when modality is set to custom. Sets the value of <tpl_name>.
  • --roi_custom_mask_pattern (optional): only used when modality is set to custom. Allows to choose a particular mask with a name following the given pattern.
  • --save_features (bool) Flag to specify if you want to save the regions as tensors in the CAPS. By default, the pipeline only extracts the images and specified regions are then extracted on-the-fly.

ROI masks

ROI masks are compressed nifti files (.nii.gz) containing a binary mask of the same size as the input data it corresponds to. All masks must follow the pattern tpl-<tpl_name>_*_roi-<roi_name>_mask.nii.gz.

If the defined region is not cubic, clinicadl prepare-data will automatically extract the smallest bounding box around the region and fill the remaining values with 0 (unless --roi_uncrop_output is specified).

Masks must correspond to the template used in the pipeline for registration. For t1-linear and pet-linear it is automatically set to MNI152NLin2009cSym. For a custom modality this value must be set using custom_template.

The chosen mask will correspond to the mask with the shortest name following the wanted pattern.

Example of a valid CAPS hierarchy:

CAPS_DIRECTORY
├── masks
│       ├── tpl-<tpl_name>
│       │       ├── tpl-<tpl_name>[_custom_pattern]_roi-<roi_1>_mask.nii.gz
│       │       ├── ...
│       │       └── tpl-<tpl_name>[_custom_pattern]_roi-<roi_N>_mask.nii.gz
│       └── tpl-MNI152NLin2009cSym
│               ├── tpl-MNI152NLin2009cSym_desc-Crop_res-1x1x1_roi-<roi_1>_mask.nii.gz
│               ├── tpl-MNI152NLin2009cSym_desc-Crop_res-1x1x1_roi-<roi_2>_mask.nii.gz
│               ├── tpl-MNI152NLin2009cSym_res-1x1x1_roi-<roi_1>_mask.nii.gz
│               └── tpl-MNI152NLin2009cSym_res-1x1x1_roi-<roi_2>_mask.nii.gz
└── subjects
        └── ...

The first two masks in tpl-MNI152NLin2009cSym/ contain desc-Crop, hence they can only be applied to cropped input images, and their size will be (169x208x179). On the contrary the last two masks in the same folder do not contain desc-Crop hence they can only be applied to uncropped input images, and their size will be (193x229x193).

The output files are <source_file>_space-<tpl_name>[_desc-{CropRoi|CropImage|Crop}][_other_descriptors]_roi-<roi_name>_<suffix>.pt: tensor version of the selected 3D region of interest. Here <source_file> corresponds to all the descriptors found in <input_pattern> before the space key. Other descriptors are computed according to the mask descriptors.

The key value following desc depends on the input and output image:

  • desc-CropROI: the input image contains desc-Crop and ROI cropping is enabled,
  • desc-CropImage: the input image contains desc-Crop and ROI cropping is disabled,
  • desc-Crop: the input image do not contain desc-Crop and ROI cropping is enabled,
  • <no_descriptor>: the input image do not contain desc-Crop and ROI cropping is disabled.

slice

Options:

  • --slice_direction: (int) slice direction. You can choose between 0 (sagittal plane), 1(coronal plane) or 2 (axial plane). Default value: 0.
  • --slice_mode: (str) slice mode. You can choose between rgb (will save the slice in three identical channels) or single (will save the slice in a single channel). Default value: rgb.
  • --discarded_slices: (int) Number of slices discarded from respectively the beginning and the end of the MRI volume.
  • --save_features (bool) Flag to specify if you want to save the slices as tensors in the CAPS. By default, the pipeline only extracts the images and specified slices are then extracted on-the-fly.

The output files are <input_pattern>_axis-{sag|cor|axi}_channel-{single|rgb}_slice-<i>_<suffix>.pt: tensor version of the <i>-th 2D slice in sagittal, coronal or axial plane using three identical channels (rgb) or one channel (single).

modality arguments

t1-linear

  • --use_uncropped_image: by default the features are extracted from the cropped image (see the documentation of the t1-linear pipeline). You can deactivate this behaviour with the --use_uncropped_image flag.

flair-linear

  • --use_uncropped_image: by default the features are extracted from the cropped image (see the documentation of the flair-linear pipeline). You can deactivate this behaviour with the --use_uncropped_image flag.

pet-linear

  • --use_uncropped_image: by default the features are extracted from the cropped image (see the documentation of the pet-linear pipeline). You can deactivate this behaviour with the --use_uncropped_image flag.
  • --tracer: the label given to the PET acquisition, specifying the tracer used (<tracer>). It can be for instance 'fdg' for 18F-fluorodeoxyglucose or 'av45' for 18F-florbetapir.
  • --suvr_reference_region: the reference region used to perform intensity normalization (i.e. dividing each voxel of the image by the average uptake in this region) resulting in a standardized uptake value ratio (SUVR) map. It can be cerebellumPons or cerebellumPons2 (used for amyloid tracers) and pons or pons2 (used for FDG). See PET introduction for more details about masks versions.

custom

  • --custom_suffix: suffix of the filename that should be converted to the tensor format. The output will be saved into a folder named custom but the processed files will kep their original name. E.g.: you can convert the images from the segmentation of the gray matter registered on the Ixi549Space. These images are obtained by running t1-volume pipeline. The suffix for these images is "graymatter_space-Ixi549Space_modulated-off_probability.nii.gz".