zea.data.convert.camus

Functionality to convert the camus dataset to the zea format.

Note

Requires SimpleITK to be installed: pip install SimpleITK.

For more information about the dataset, resort to the following links:

  • The original dataset can be found at this link.

Functions

convert_camus(args)

Converts the CAMUS dataset into ZEA HDF5 files across dataset splits.

get_split(patient_id)

Determine which dataset split a patient ID belongs to.

process_camus(source_path, output_path[, ...])

Converts the camus database to the zea format.

sitk_load(filepath)

Loads an image using SimpleITK and returns the image and its metadata.

transform_sc_image_to_polar(image_sc[, ...])

Transform a scan converted input image (cone) into square
zea.data.convert.camus.convert_camus(args)[source]

Converts the CAMUS dataset into ZEA HDF5 files across dataset splits.

Processes files found under the CAMUS source folder (after unzipping if needed), assigns each patient to a train/val/test split, creates matching output paths, and executes per-file conversion tasks either serially or in parallel. Ensures output directories do not pre-exist, and logs progress and failures.

Parameters:

args (argparse.Namespace) –

An object with attributes:

  • src (str | Path): Path to the CAMUS archive or extracted folder.

  • dst (str | Path): Root destination folder for ZEA HDF5 outputs; split subfolders will be created.

  • no_hyperthreading (bool, optional): If True, run tasks serially instead of using a process pool.

zea.data.convert.camus.get_split(patient_id)[source]

Determine which dataset split a patient ID belongs to.

Parameters:

patient_id (int) – Integer ID of the patient.

Returns:

“train”, “val”, or “test”.

Return type:

The split name

Raises:

ValueError – If the patient_id does not fall into any defined split range.

zea.data.convert.camus.process_camus(source_path, output_path, overwrite=False)[source]

Converts the camus database to the zea format.

Parameters:

  • source_path (str, pathlike) – The path to the original camus file.

  • output_path (str, pathlike) – The path to the output file.

  • overwrite (bool, optional) – Set to True to overwrite existing file. Defaults to False.

zea.data.convert.camus.sitk_load(filepath)[source]

Loads an image using SimpleITK and returns the image and its metadata.

Parameters:

filepath (str | Path) – Path to the image.

Return type:

Tuple[ndarray, Dict[str, Any]]

Returns:

  • Image array of shape (num_frames, height, width).

  • Collection of metadata.

zea.data.convert.camus.transform_sc_image_to_polar(image_sc, output_size=None, fit_outline=True)[source]
Transform a scan converted input image (cone) into square

using radial stretching and downsampling. Note that it assumes the background to be zero! Please verify if your results make sense, especially if the image contains black parts at the edges. This function is not perfect by any means, but it works for most cases.

Parameters:

  • image (numpy.ndarray) – Input image as a 2D numpy array (height, width).

  • output_size (tuple, optional) – Output size of the image as a tuple. Defaults to image_sc.shape.

  • fit_outline (bool, optional) – Whether to fit a polynomial the outline of the image. Defaults to True. If this is set to False, and the ultrasound image contains some black parts at the edges, weird artifacts can occur, because the jagged outline is stretched to the desired width.

Returns:

Squared image as a 2D numpy array (height, width).

Return type:

numpy.ndarray