`segmentation` - Compute binary voxel masks from contours¶

This transform relies on the Python packages shapely and rasterio. The external and internal carotids are combined after this transform.

Illustration of segmentation transform

Prerequisites¶

This step relies on the outputs of transform contour.

JSON parameters

This step does not only require the contour TSV files but will try to load and update the parameters.json file in the directory in which contours are stored. Make sure this file exists at the root of the contours directory.

Running the task¶

The task can be run with the following command line:

carotid [-v] transform segmentation OUTPUT_DIR

where:

OUTPUT_DIR (str) is the path to the directory containing the outputs.

Options:

--contour_dir (str) is the path to a different directory in which the contours are stored. Default will assume that transform contour was run in the output directory.
--config_path (str) is the path to a config file defining the values of the parameters.
--participant (List[str]) restricts the application of the transform to this list of participant IDs. Default will perform the pipeline on all participants with a raw image.
--force is a flag that forces the application of the transform in the chosen output directory, even if the transform was already performed in this folder.

verbosity

To increase the verbosity of the algorithm, add -v right between carotid and transform. Debug outputs can be obtained with -vv.

Default parameters¶

There are no parameters for this transform.

Outputs¶

Output structure for participant participant_id:

<output_dir>
├── parameters.json
└── <participant_id>
        └── contour_transform
                ├── left_segmentation.mha
                └── right_segmentation.mha