spateo.segmentation.external.stardist
=====================================

.. py:module:: spateo.segmentation.external.stardist

.. autoapi-nested-parse::

   Use StarDist for cell identification and labeling.
   https://github.com/stardist/stardist

   [Schmidt18]_ and [Weigert20]_



Attributes
----------

.. autoapisummary::

   spateo.segmentation.external.stardist.StarDist2D


Functions
---------

.. autoapisummary::

   spateo.segmentation.external.stardist._stardist
   spateo.segmentation.external.stardist._stardist_big
   spateo.segmentation.external.stardist._sanitize_labels
   spateo.segmentation.external.stardist.stardist


Module Contents
---------------

.. py:data:: StarDist2D
   :value: None


.. py:function:: _stardist(img: numpy.ndarray, model: Union[typing_extensions.Literal[2D_versatile_fluo, 2D_versatile_he, 2D_paper_dsb2018], stardist.models.StarDist2D] = '2D_versatile_fluo', **kwargs) -> numpy.ndarray

   Run StarDist on the provided image.

   :param img: Image as a Numpy array.
   :param model: Stardist model to use. Can be one of the three pretrained models
                 from StarDist2D:
                 1. '2D_versatile_fluo': 'Versatile (fluorescent nuclei)'
                 2. '2D_versatile_he':  'Versatile (H&E nuclei)'
                 3. '2D_paper_dsb2018': 'DSB 2018 (from StarDist 2D paper)'
                 Or any generic Stardist2D model.
   :param \*\*kwargs: Additional keyword arguments to :func:`StarDist2D.predict_instances`
                      function.

   :returns: Numpy array containing cell labels.


.. py:function:: _stardist_big(img: numpy.ndarray, model: Union[typing_extensions.Literal[2D_versatile_fluo, 2D_versatile_he, 2D_paper_dsb2018], stardist.models.StarDist2D] = '2D_versatile_fluo', **kwargs) -> numpy.ndarray

   Run StarDist on the provided image.

   :param img: Image as a Numpy array.
   :param model: Stardist model to use. Can be one of the three pretrained models
                 from StarDist2D:
                 1. '2D_versatile_fluo': 'Versatile (fluorescent nuclei)'
                 2. '2D_versatile_he':  'Versatile (H&E nuclei)'
                 3. '2D_paper_dsb2018': 'DSB 2018 (from StarDist 2D paper)'
                 Or any generic Stardist2D model.
   :param \*\*kwargs: Additional keyword arguments to :func:`StarDist2D.predict_instances_big`
                      function.

   :returns: Numpy array containing cell labels.


.. py:function:: _sanitize_labels(labels: numpy.ndarray) -> numpy.ndarray

   Sanitize labels obtained from StarDist.

   StarDist sometimes yields disconnected labels. This function removes
   these problems by selecting the largest area.

   :param labels: Numpy array containing labels

   :returns: Sanitized labels.


.. py:function:: stardist(adata: anndata.AnnData, model: Union[typing_extensions.Literal[2D_versatile_fluo, 2D_versatile_he, 2D_paper_dsb2018], stardist.models.StarDist2D] = '2D_versatile_fluo', tilesize: int = 2000, min_overlap: Optional[int] = None, context: Optional[int] = None, normalizer: Optional[csbdeep.data.Normalizer] = PercentileNormalizer(), equalize: float = 2.0, sanitize: bool = True, layer: str = SKM.STAIN_LAYER_KEY, out_layer: Optional[str] = None, **kwargs)

   Run StarDist to label cells from a staining image.

   .. note::

      When using `min_overlap`, the crucial assumption is that all predicted
      object instances are smaller than the provided `min_overlap`.
      Also, it must hold that: min_overlap + 2*context < tilesize.
      https://github.com/stardist/stardist/blob/858cae17cf17f979122000ad2294a156d0547135/stardist/models/base.py#L776

   :param adata: Input Anndata
   :param img: Image as a Numpy array.
   :param model: Stardist model to use. Can be one of the three pretrained models
                 from StarDist2D:
                 1. '2D_versatile_fluo': 'Versatile (fluorescent nuclei)'
                 2. '2D_versatile_he':  'Versatile (H&E nuclei)'
                 3. '2D_paper_dsb2018': 'DSB 2018 (from StarDist 2D paper)'
                 Or any generic Stardist2D model.
   :param tilesize: Run prediction separately on tiles of size `tilesize` x `tilesize`
                    and merge them afterwards. Useful to avoid out-of-memory errors. Can be
                    set to <= 0 to disable tiling. When `min_overlap` is also provided, this
                    becomes the `block_size` parameter to :func:`StarDist2D.predict_instances_big`.
   :param min_overlap: Amount of guaranteed overlaps between tiles.
   :param context: Amount of image context on all sides of a tile, which is dicarded.
                   Only used when `min_overlap` is not None. By default, an automatic
                   estimate is used.
   :param normalizer: Normalizer to use to perform normalization prior to prediction.
                      By default, percentile-based normalization is performed. `None` may
                      be provided to disable normalization.
   :param equalize: Controls the `clip_limit` argument to the :func:`clahe` function.
                    Set this value to a non-positive value to turn off equalization.
   :param sanitize: Whether to sanitize disconnected labels.
   :param layer: Layer that contains staining image. Defaults to `stain`.
   :param out_layer: Layer to put resulting labels. Defaults to `{layer}_labels`.
   :param \*\*kwargs: Additional keyword arguments to pass to :func:`StarDist2D.predict_instances`.