liom_toolkit.segmentation.stats module

liom_toolkit.segmentation.stats.calculate_density(vessel_mask: ndarray, mask: ndarray, voxel_size: float = 0.65) → tuple[float, float, float]

Calculates the areas of the tissue and vessel to compute the density of vessels in a mask.

Parameters:

  • vessel_mask (np.ndarray) – The mask of the vessels.

  • mask (np.ndarray) – The mask of the tissue.

  • voxel_size (float) – The size of the voxels in the image.

Returns:

The area of the tissue, the area of the vessels, and the density of the vessels.

Return type:

tuple[float, float, float]

liom_toolkit.segmentation.stats.calculate_regional_density(region: ndarray, region_index: int, props_list: list[RegionProperties], output_dir: str, voxel_size: float = 0.65) → tuple[float, float, float]

Calculates the density of vessels in a region

Parameters:

  • region (np.ndarray) – The region to calculate the density of.

  • region_index (int) – The computational index of the region.

  • props_list (list[RegionProperties]) – The list of properties of the regions.

  • output_dir (str) – The directory to save the region mask to.

  • voxel_size (float) – The size of the voxels in the image.

Returns:

The area of the vessels, the area of the region, and the density of the vessels in a specific region.

Return type:

tuple[float, float, float]

liom_toolkit.segmentation.stats.compute_average_diameter(mask: ndarray, skeleton: ndarray, voxel_size: float = 0.65) → float

Compute the average diameter of the vessels in a mask

Parameters:

  • mask (np.ndarray) – The vessel mask.

  • skeleton (np.ndarray) – The skeleton of the vessels.

  • voxel_size (float) – The size of the voxels in the image.

Returns:

The average diameter of the vessels in the mask.

Return type:

float

liom_toolkit.segmentation.stats.compute_mask_area(mask: Array | Future) → uint64

Compute the area of a mask. Sums the binary masks.

Parameters:

mask (da.Array | Future) – The mask to compute the area of.

Returns:

The area of the mask.

Return type:

np.uint64

liom_toolkit.segmentation.stats.compute_slice_metrics(output_dir: str, image: ndarray, mask: ndarray, vessel_mask: ndarray, region_map: ndarray, vessel_exclude: ndarray, voxel_size: float = 0.65) → None

Compute the metrics for a brain slice. Save the results to disk.

Parameters:

  • output_dir (str) – The directory to save the output to

  • image (np.ndarray) – The image of the brain slice

  • mask (np.ndarray) – The mask of the tissue in the brain slice

  • vessel_mask (np.ndarray) – The mask of the vessels in the brain slice

  • region_map (np.ndarray) – The map of the regions in the brain slice

  • vessel_exclude (np.ndarray) – The mask of the vessels to exclude from the analysis

  • voxel_size (float) – The size of the voxels in the image

liom_toolkit.segmentation.stats.create_filter_image(atlas: Array | Future, region_ids: list[int]) → Array

Create a filter image based on the region ids.

Parameters:

  • atlas (da.Array | Future) – The atlas containing the region ids.

  • region_ids (list[int]) – The region ids to filter.

Returns:

The filter image.

Return type:

da.Array

liom_toolkit.segmentation.stats.create_heatmap(image: ndarray, output_dir: str, square_size: int = 150) → None

Create and save a heatmap of the vessel density in a brain slice and save it to disk.

Parameters:

  • image (np.ndarray) – The image of the brain slice.

  • output_dir (str) – The directory to save the heatmap to.

  • square_size (int) – The size of the squares in the heatmap

liom_toolkit.segmentation.stats.draw_branch_point_circles(skeleton: ndarray, branching_points: ndarray, output_dir: str, filename: str = 'skeleton_circled.png') → None

Draw circles around the branching points in a skeleton

Parameters:

  • skeleton (np.ndarray) – The skeleton of the vessels.

  • branching_points (np.ndarray) – The location of the branching points.

  • output_dir (str) – The directory to save the skeleton to.

  • filename (str) – The filename to save the skeleton to.

Returns:

The circled branching point in the skeleton.

Return type:

np.ndarray

liom_toolkit.segmentation.stats.filter_image_to_region(image_filter: Array, data: Array | Future) → Array

Filter an image to a region based on a filter.

Parameters:

  • image_filter (da.Array) – The filter to apply.

  • data (da.Array | Future) – The data to filter.

Returns:

The filtered image.

Return type:

da.Array

liom_toolkit.segmentation.stats.generate_itk_id_list_of_region(region: str, data_dir='') → list[int]

Generate a list of itk ids for a given region. Will reconstruct the structure tree and get the descendants contained within the region

Parameters:

  • region (str) – The region to get the ids for.

  • data_dir (str) – The directory where the atlas and structure tree are saved. Optional.

Returns:

The list of itk ids for the region and its descendants.

Return type:

list[int]

liom_toolkit.segmentation.stats.get_branching_point_count(vessel_mask: ndarray, output_dir: str, filename: str = 'skeleton.tif') → tuple[int, ndarray, ndarray]

Get the number of branching points in a vessel mask.

Parameters:

  • vessel_mask (np.ndarray) – The mask of the vessels.

  • output_dir (str) – The directory to save the skeleton to.

  • filename (str) – The filename to save the skeleton to.

Returns:

The number of branching points in the vessel mask, the skeleton of the vessel mask, and the location of the branching points.

Return type:

tuple[int, np.ndarray, np.ndarray]

liom_toolkit.segmentation.stats.get_branching_points(skeleton: ndarray) → ndarray

Get the branching points in a skeleton using predefined structural elements Source: https://stackoverflow.com/questions/43037692/how-to-find-branch-point-from-binary-skeletonize-image

Parameters:

skeleton (np.ndarray) – The skeleton of the vessels.

Returns:

The branching points in the skeleton.

Return type:

np.ndarray

liom_toolkit.segmentation.stats.get_vessel_region(regions: ndarray, region_index: int, vessel_mask: ndarray) → ndarray

Get the vessels in a region.

Parameters:

  • regions (np.ndarray) – The regions of the tissue mask.

  • region_index (int) – The index of the region.

  • vessel_mask (np.ndarray) – The mask of the vessels.

Returns:

The vessel within the masked region.

Return type:

np.ndarray