polar - Draw 3D polar images

This step generates one polar image per center found in the previous step.

First a local patch around the center is extracted from the original image. Then a polar transform is applied to this local patch: in the cartesian space n_angles rays of size cartesian_ray are drawn. Along each of these rays a number of polar_ray values are interpolated. Each ray is sampled twice, resulting in a 2D polar image of size (2*n_angles, polar_ray). This operation is done for the length neighbouring axial slices, resulting in a 3D polar image of size (1, 1, length, 2*n_angles, polar_ray) after addition of the channel and batch dimension.

Example of polar image extracted. The center is highlighted in blue, and an example of ray drawn in the cartesian space is highlighted in orange.

PyTorch version

This step gives different results depending on the PyTorch version used. The results in the original paper were obtained with PyTorch 1, and the current version of the repo is using Pytorch 2, however the results obtained with the full pipeline are still comparable (Dice score > 0.95 for all contours).

Prerequisites

This step relies on the outputs of transform centerline and raw images. The path to raw images is found on the JSON file listing all parameters.

JSON parameters

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

Running the task

The task can be run with the following command line:

carotid [-v] transform polar OUTPUT_DIR

where:

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

Options:

• --centerline_dir (str) is the path to a different directory in which the centerlines are stored. Default will assume that transform centerline 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

The following values can be chosen by the user:

• n_angles (int) Number of rays that are interpolated. Default: 31.
• polar_ray (int) Size of the ray in the polar space. Default: 127.
• cartesian_ray (int) Size of the ray in the cartesian space. Default: 100.
• length (int) Number of axial slices used for one polar image. Default: 7.
• multiple_centers (bool) if True, a batch of 3D polar images of size (9, 1, length, 2*n_angles, polar_ray) will be extracted. This 9 images correspond to the polar transform according to the 8 voxels around the original center in the given axial plane + the original center. Default is False.

Outputs

Output structure for participant participant_id:

<output_dir>
├── parameters.json
└── <participant_id>
        └── polar_transform
                ├── left_polar
                │       ├── label-<internal|external>_slice-<slice_idx>_center.npy
                │       ├── label-<internal|external>_slice-<slice_idx>_polar.npy
                │       ├── ...
                │       └── parameters.json
                └── right_polar

where:

• parameters.json is a JSON file summarizing the parameters used to perform this transform and eventually preceding ones.
• <side>_polar is a folder containing the following objects:
• label-<internal|external>_slice-<slice_idx>_center.npy is the numpy array of the center coordinates of the polar image corresponding to the internal or external carotid on slice number slice_idx.
• label-<internal|external>_slice-<slice_idx>_polar.npy is the numpy array corresponding to a batch of 3D polar images extracted using the associated center.
• parameters.json stores meta-data associated to the raw image (spatial shape and affine).