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Creating Preprocessing Pipelines
================================
Preprocessing pipelines define how raw images are transformed and prepared for downstream analysis.
The ``pathml.preprocessing`` module provides tools to define modular preprocessing pipelines for whole-slide images.
In this section we will walk through how to define a
:class:`~pathml.preprocessing.pipeline.Pipeline` object by composing pre-made
:class:`~pathml.preprocessing.transforms.Transform` objects, and how to implement a
new custom :class:`~pathml.preprocessing.transforms.Transform`.
What is a Transform?
--------------------
The :class:`~pathml.preprocessing.transforms.Transform` is the building block for creating preprocessing pipelines.
Each :class:`~pathml.preprocessing.transforms.Transform` applies a specific operation to a
:class:`~pathml.core.tile.Tile` which may include modifying
an input image, creating or modifying pixel-level metadata (i.e., masks), or creating or modifying image-level metadata
(e.g., image quality metrics or an AnnData counts matrix).
.. figure:: _static/images/transform_schematic.png
:alt: schematic diagram of Transform
:scale: 70 %
:align: center
Schematic diagram of a :class:`~pathml.preprocessing.transforms.Transform` operating on a tile.
In this example, several masks are created (represented by stacked rectangles) as well as
several labels (depicted here as cubes).
.. figure:: _static/images/transforms.png
:alt: examples of Transforms
:scale: 75 %
:align: center
Examples of several types of :class:`~pathml.preprocessing.transforms.Transform`
What is a Pipeline?
-----------------------
A preprocessing pipeline is a set of independent operations applied sequentially.
In ``PathML``, a :class:`~pathml.preprocessing.pipeline.Pipeline` is defined as a sequence of
:class:`~pathml.preprocessing.transforms.Transform` objects. This makes it easy to compose a custom
:class:`~pathml.preprocessing.pipeline.Pipeline` by mixing-and-matching:
.. figure:: _static/images/pipeline_composition_schematic.png
:alt: schematic diagram of modular pipeline composition
:scale: 40 %
:align: center
Schematic diagram of :class:`~pathml.preprocessing.pipeline.Pipeline` composition from a set of modular components
In the PathML API, this is concise:
.. code-block::
from pathml.preprocessing import Pipeline, BoxBlur, TissueDetectionHE
pipeline = Pipeline([
BoxBlur(kernel_size=15),
TissueDetectionHE(mask_name = "tissue", min_region_size=500,
threshold=30, outer_contours_only=True)
])
In this example, the preprocessing pipeline will first apply a box blur kernel, and then apply tissue detection.
Creating custom Transforms
------------------------------
.. note:: For advanced users
In some cases, you may want to implement a custom :class:`~pathml.preprocessing.transforms.Transform`.
For example, you may want to apply a transformation which is not already implemented in ``PathML``.
Or, perhaps you want to create a new transformation which combines several others.
To define a new custom :class:`~pathml.preprocessing.transforms.Transform`,
all you need to do is create a class which inherits from :class:`~pathml.preprocessing.transforms.Transform` and
implements an ``apply()`` method which takes a :class:`~pathml.core.tile.Tile` as an argument and modifies it in place.
You may also implement a functional method ``F()``, although that is not strictly required.
For example, let's take a look at how :class:`~pathml.preprocessing.transforms.BoxBlur` is implemented:
.. code-block:: python
class BoxBlur(Transform):
"""Box (average) blur kernel."""
def __init__(self, kernel_size=5):
self.kernel_size = kernel_size
def F(self, image):
return cv2.boxFilter(image, ksize = (self.kernel_size, self.kernel_size), ddepth = -1)
def apply(self, tile):
tile.image = self.F(tile.image)
Once you define your custom :class:`~pathml.preprocessing.transforms.Transform`,
you can plug it in with any of the other :class:`~pathml.preprocessing.transforms.Transform`s,
compose :class:`~pathml.preprocessing.pipeline.Pipeline`, etc.
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