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pipeline.md

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Pipeline

The BodyComposition package can be used to run different pipelines. Pipelines are registred in the pipeline_registry.py file. If a registred pipeline is run, the specific pipeline configuration is loaded from the pipelines directory.

Each pipeline is a sequence of stages that are executed in order as defined in the respective pipeline file. Each stage is defined as action, which are classes that are derived from the PipelineAction class.

At the start of the pipeline, each action class is initialized. The pipeline selects all cases, where all required inputs (defined by the individual actions) are available. Then, for each case, all actions are executed in order. Outputs of actions are available to the next actions in the pipeline using a shared memory dictionary.

Pipelines

While pipelines can be customized by the user, the following methods are readily implemented:

Actions

Segmentation

  • SegmIntVertebrae: Segments the vertebral body using nnU-Net with models described here. Requires image being a NIfTI data container and model being a string defining either the ResEncM or ResEncL model. Returns (and optionally saves) the segmentation as a NIfTI data container.
  • SegmStanfordSpine: Segments the vertebral body using the Comp2Comp Spine Segmentation model. Requires image being a NIfTI data container. Returns (and optionally saves) the segmentation as a NIfTI data container.
  • SegmTotalSegmentator: Segments any structures using the TotalSegmentator. Requires image being a NIfTI data container, task being a string defining the task to be performed (e.g., spine, bodytrunk, tissue, vertebralbodies, iliospoas), and fast being a boolean defining whether TotalSegmentator's fast function should be used. Before running this action, TotalSegmentator must be initialized using the SegmTotalSegmentatorConfig action. Returns (and optionally saves) the segmentation as a NIfTI data container.

Masks Spine

  • MasksTotalSegmentatorSpine: Maps the TotalSegmentator labels to the standard labels used in the pipeline. If reduce_to_vb is set to True, the labels are reduced to the vertebral bodies using TotalSegmentator's vertebral_body segmentation. Returns the remapped masks as a NIfTI data container.
  • MasksStanfordSpine: Maps the Comp2Comp Spine labels to the standard labels used in the pipeline. If reduce_to_vb is set to True, the labels are reduced to the vertebral bodies using TotalSegmentator's vertebral_body segmentation. Returns the remapped masks as a NIfTI data container.

Masks Tissue

During the processing of tissue masks, filters based on Hounsfield units and 2D or 3D size properties are applied to subsegment labels and generate masks as explained here and defined in the pipeline's configuration.

  • MasksTotalSegmentatorTissue: Maps the TotalSegmentator labels to the standard labels used in the pipeline. If iliopsoas is set to True, the a separate label of the iliopsoas muscle is returned using TotalSegmentator's iliopsoas segmentation. If bodytrunk is set to True, the labels are reduced to the body trunk using TotalSegmentator's bodytrunk segmentation. Returns the remapped masks as a NIfTI data container.

Bounding Boxes

  • CreateBoundingBox: Creates bounding boxes around specific labels. The segmentation label must be provided as a NIfTI data container. The bounding box is defined in the pipeline's configuration, and the specific task must be defined as argument. The function then creates a bounding box around the specific label and saves it (bbox) to the memory dictionary.
  • ApplyBoundingBox: Applies a bounding box to a image, label or mask. The segmentation label must be provided as NIfTI data containers, and the bounding box bbox must be available within the memory dictionary. If the NIfTI data container should not be changed, but saved separately, define its name using the output argument. The function then applies the bounding box to the input NIfTI. If the changed NIfTI data container is used later on, only values within the bounding box are returned, changed or saved.

Postprocessing

  • CalcVertebralLevel: Calculates the vertebral levels based based on a (reorientated) mask refering to a NIfTI data container containing the (postprocessed) vertebral body segmentations. For each slice, the dominating vertebral body (most pixels) is determined using settings as defined in the pipeline's configuration. Returns a numpy array containing the vertebral levels (tmp/vertebrae_values) to the memory dictionary.
  • CalcCSA: Calculates the cross-sectional area (CSA) of the tissues based on a (reorientated) mask refering to a NIfTI data container containing the (postprocessed) tissue segmentations. The CSA is calculated for each label in cm², considering the settings as defined in the pipeline's configuration. Returns a numpy array containing the CSA values (tmp/tissue_values) to the memory dictionary.

Data Handling

  • LoadMetadata: Trys to load metadata. The path is given as an argument, with the placeholder {caseid} being replaced by the current cases id. Can be both, a *csv (containing DICOM metadata) or a *dcm file. The metadata is saved to the memory dictionary as tmp/metadata.
  • DataCombine: Trys to combine tissue measurements (CSA) and vertebral levels. Checks whether affine, spacing and other metadata match. Returns a pandas dataframe containing the combined data (tmp/bodycomposition) to the memory dictionary.
  • DataSubset: Can be used to create a subset of tmp/bodycomposition (or an other df as defined as input_df argument) for later aggregation. The subset is defined by a reference (Center, Level, Centroid, Tag) corresponding to the vertebral levels created by CalcVertebralLevel and a specific vertebral level (ALL for all vertebrae, L for all lumbar vertebrae, or a string or list defining specific vertebrae). The subset is saved to the memory dictionary as tmp/bodycomposition or a specific name defined by the output_df argument.
  • DataAggregate: Aggregates the data in tmp/bodycomposition (or an other df as defined as input_df argument). Groups are defined by a reference ref (Center, Level, Centroid, Tag) corresponding to the vertebral levels created by CalcVertebralLevel. If individual groups are required, individual groups can be defined using the tag_mapping dictionary that should map the values from ref to new, individual groups "tags". The method of aggregation is defined by method, currently mean, median and sum are supported. The aggregated data is saved to the memory dictionary as tmp/bodycomposition or a specific name defined by the output_df argument.
  • DataExport: Saves data from a pandas dataframe (defined as argument input) to a csv file (defined as argument file, using placeholder {caseid}). If add_metadata is set to True, the metadata imported by LoadMetadata is concatenated. If append is set to True, the data is appended to the file which can be useful to generate summary files of multiple cases. If add_header is set to True, the header is added to the exported data. If add_index is set to True, the index is added to the exported data.