histocartography.preprocessing.tissue_mask module

Summary

Classes:

AnnotationPostProcessor

GaussianTissueMask

Helper class to extract tissue mask from images

TissueMask

Functions:

get_tissue_mask

Get binary tissue mask
get_tissue_mask(image: numpy.ndarray, n_thresholding_steps: int = 1, sigma: float = 0.0, min_size: int = 500) → Tuple[numpy.ndarray, numpy.ndarray][source]

Get binary tissue mask

Parameters

  • image (np.ndarray) – (m, n, 3) nd array of thumbnail RGB image or (m, n) nd array of thumbnail grayscale image

  • n_thresholding_steps (int, optional) – number of gaussian smoothign steps. Defaults to 1.

  • sigma (float, optional) – sigma of gaussian filter. Defaults to 0.0.

  • min_size (int, optional) – minimum size (in pixels) of contiguous tissue regions to keep. Defaults to 500.

Returns

np int32 array

each unique value represents a unique tissue region

np bool array

largest contiguous tissue region.

Return type

Tuple[np.ndarray, np.ndarray]

class TissueMask(save_path: Union[None, str, pathlib.Path] = None, precompute: bool = True, link_path: Union[None, str, pathlib.Path] = None, precompute_path: Union[None, str, pathlib.Path] = None)[source]

Bases: histocartography.pipeline.PipelineStep

precompute(link_path: Union[None, str, pathlib.Path] = None, precompute_path: Union[None, str, pathlib.Path] = None) → None[source]

Precompute all necessary information

Parameters

  • link_path (Union[None, str, Path], optional) – Path to link to. Defaults to None.

  • precompute_path (Union[None, str, Path], optional) – Path to save precomputation outputs. Defaults to None.

class GaussianTissueMask(n_thresholding_steps: int = 1, sigma: int = 20, min_size: int = 10, kernel_size: int = 20, dilation_steps: int = 1, background_gray_value: int = 228, downsampling_factor: int = 4, **kwargs)[source]

Bases: histocartography.preprocessing.tissue_mask.TissueMask

Helper class to extract tissue mask from images

__init__(n_thresholding_steps: int = 1, sigma: int = 20, min_size: int = 10, kernel_size: int = 20, dilation_steps: int = 1, background_gray_value: int = 228, downsampling_factor: int = 4, **kwargs) → None[source]
Parameters

  • n_thresholding_steps (int, optional) – Number of gaussian smoothing steps. Defaults to 1.

  • sigma (int, optional) – Sigma of gaussian filter. Defaults to 20.

  • min_size (int, optional) – Minimum size (in pixels) of contiguous tissue regions to keep. Defaults to 10.

  • kernel_size (int, optional) – Dilation kernel size. Defaults to 20.

  • dilation_steps (int, optional) – Number of dilation steps. Defaults to 1.

  • background_gray_value (int, optional) – Gray value of background pixels (usually high). Defaults to 228.

  • downsampling_factor (int, optional) – Downsampling factor from the input image resolution. Defaults to 4.

class AnnotationPostProcessor(background_index: int, **kwargs: Any)[source]

Bases: histocartography.pipeline.PipelineStep

mkdir() → pathlib.Path[source]

Create path to output files

Reference

If you use histocartography in your projects, please cite the following:

@inproceedings{pati2021,
    title = {Hierarchical Graph Representations for Digital Pathology},
    author = {Pushpak Pati, Guillaume Jaume, Antonio Foncubierta, Florinda Feroce, Anna Maria Anniciello, Giosuè Scognamiglio, Nadia Brancati, Maryse Fiche, Estelle Dubruc, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Jean-Philippe Thiran, Maria Frucci, Orcun Goksel, Maria Gabrani},
    booktitle = {https://arxiv.org/pdf/2102.11057},
    year = {2021}
}