diff --git a/.github/workflows/mkdocs-build.yml b/.github/workflows/mkdocs-build.yml new file mode 100644 index 0000000..7cc105b --- /dev/null +++ b/.github/workflows/mkdocs-build.yml @@ -0,0 +1,11 @@ +name: MkDocs Build (RationAI Standard) +on: + push: + branches: ["master"] + pull_request: + branches: ["master"] + workflow_dispatch: + +jobs: + run: + uses: RationAI/.github/.github/workflows/mkdocs-build.yml@main diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml new file mode 100644 index 0000000..6bf101f --- /dev/null +++ b/.github/workflows/python-lint.yml @@ -0,0 +1,11 @@ +name: Python Lint (RationAI Standard) +on: + push: + branches: ["master"] + pull_request: + branches: ["master"] + workflow_dispatch: + +jobs: + run: + uses: RationAI/.github/.github/workflows/python-lint.yml@main diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index f6d4011..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,9 +0,0 @@ -include: - project: rationai/digital-pathology/templates/ci-templates - file: - - Python-Lint.gitlab-ci.yml - - MkDocs.gitlab-ci.yml - -stages: - - lint - - deploy diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 0000000..495a1eb --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,6 @@ +MD013: false # Line length +MD023: false # Heading start left +MD024: false # Duplicate headings +MD033: false # Inline HTML +MD041: false # First line in file should be a top level heading +MD046: false # Fenced code blocks \ No newline at end of file diff --git a/.mypy.ini b/.mypy.ini index 8d15f3c..e39d779 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -3,4 +3,8 @@ strict = True ignore_missing_imports = True disallow_untyped_calls = False disable_error_code = no-any-return -explicit_package_bases = True \ No newline at end of file +explicit_package_bases = True + +# Ignore type errors in the external piqe.py file +[mypy-rationai.qc.blur.piqe] +ignore_errors = True \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6a4813b..65cb974 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,16 +8,15 @@ repos: args: [--unsafe] - repo: https://github.com/commitizen-tools/commitizen - rev: v4.8.3 + rev: v4.10.0 hooks: - id: commitizen - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.10 + rev: v0.14.10 hooks: # Run the linter. - - id: ruff - entry: pdm lint --force-exclude + - id: ruff-check + args: [--fix] # Run the formatter. - id: ruff-format - entry: pdm format --force-exclude diff --git a/.ruff.toml b/.ruff.toml index fb5e992..661f7ef 100644 --- a/.ruff.toml +++ b/.ruff.toml @@ -2,6 +2,8 @@ fix = true line-length = 88 target-version = "py311" +extend-exclude = ["rationai/qc/blur/piqe.py"] + [format] # Enable reformatting of code snippets in docstrings. docstring-code-format = true @@ -20,10 +22,10 @@ extend-select = [ "PGH", # pygrep "RUF", # ruff checks "SIM", # flake8-simplify - "TCH", # flake8-type-checking + "TC", # flake8-type-checking "TID", # flake8-tidy-imports "UP", # pyupgrade - "D", # pydocstyle + "D", # pydocstyle ] extend-ignore = [ "ERA001", # commented out code @@ -36,7 +38,7 @@ extend-ignore = [ "D106", # missing docstring in public nested class "D107", # missing docstring in __init__ "N812", # lowercase imported as non lowercase - "TCH002", # move third-party into a type-checking block + "TC002", # move third-party into a type-checking block "F722", # jaxtyping ] diff --git a/README.md b/README.md index f4f3ef9..6893e5e 100644 --- a/README.md +++ b/README.md @@ -5,4 +5,21 @@ for QC methods** developed at RationAI. These functions are meant to be run on s regions and they **offer a straightforward and well-documented access** to the key parts of the complete QC methods to allow for easier debugging and experimenting. -TODO: Add link to mkdocs documentation \ No newline at end of file +## Documentation + +**[`RationAI Quality Control`](https://rationai.github.io/quality-control/)** + +## Setting up a Development Environment + +1. Create the development environment (including all optional and development dependencies): + + ```bash + uv sync --all-groups + ``` + +2. Install pre-commit hooks: + + ```bash + uv run pre-commit install + uv run pre-commit install --hook-type commit-msg + ``` diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 4489bfd..56e8b54 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,4 +1,4 @@ -1. ### Install **pyvips** for Efficient Image Processing +1. ### Install **pyvips** for Efficient Image Processing [Pyvips](https://pypi.org/project/pyvips/2.0.2/) is a powerful and fast image processing library, particularly suited for working with large images like WSIs. To install `pyvips`, ensure that the `libvips` system dependencies are installed first: @@ -18,41 +18,21 @@ Follow the instructions on the official [libvips installation page](https://www.libvips.org/install.html). - -2. ### Install the Core Quality Control Library +2. ### Install the Core Quality Control Library You can install the core library using one of the following methods, depending on your package manager: - === "pdm" - - ```bash - pdm add git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git - ``` - - === "pip" - - ```bash - pip install git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git - ``` - - #### Installing a Specific Version - - To install a specific version of the **Quality Control** library, use the following command, replacing `v1.0.0` with your desired version. - - === "pdm" + === "uv" + (*Note that this command assumes an existing uv project.*) ```bash - pdm add git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git@v1.0.0 + uv add git+https://github.com/RationAI/quality-control.git ``` === "pip" ```bash - pip install git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git@v1.0.0 + pip install git+https://github.com/RationAI/quality-control.git ``` - !!! info - - Replace `v1.0.0` with the specific version you want to install. - Once **pyvips**, and the core library are installed, you're ready to start using QC functions. diff --git a/docs/getting-started/quality-control.md b/docs/getting-started/quality-control.md index 78a7b3f..d7a11c5 100644 --- a/docs/getting-started/quality-control.md +++ b/docs/getting-started/quality-control.md @@ -1,40 +1,45 @@ # Using the Provided Quality Control Functions + This document presents several examples of using the provided QC functions. These examples -should provide you with good overview of the offered functionality and potential limitations. +should provide you with a good overview of the offered functionality and potential limitations. ## Suggested Workflow + This library is not suited for running the provided QC methods on complete WSIs. Instead, the functions are expected to be run on smaller WSI regions and regular PNG/JPG images. This should provide reasonable options for debugging potential problems, while avoiding the technical overhead needed for processing the complete WSIs. ## Examples -The following examples should provide you with the neccessary information for running the provided QC functions. + +The following examples should provide you with the necessary information for running the provided QC functions. ### Detecting Residual Artifacts + In this example, we will work with the following image: ![The input image](data/residual_input.png) -#### Sample code +#### Sample Code + First, we need to import the necessary functionality and load the image into numpy array: ```python linenums="1" import numpy as np from PIL import Image - from rationai.qc import residual_artifacts_and_coverage -from rationai.staining import ColorConversion +from rationai.staining import StandardConversions img = np.asarray(Image.open("input.png").convert("RGB")) ``` -Next, we will need to prepare the arguments for the [residual_artifacts_and_coverage](../api/residual_artifacts/residual-artifacts-and-coverage.md) function and call it on the input image: +Next, we will need to prepare the arguments for the [residual_artifacts_and_coverage](../api/residual_artifacts/residual-artifacts-and-coverage.md) +function and call it on the input image: -```python linenums="10" +```python linenums="9" -conversion = ColorConversion.RGB2HER +conversion = StandardConversions.RGB2HER nucleus_area = 150 res_index = 2 threshold = 0.005 @@ -46,41 +51,42 @@ artifacts = residual_artifacts_and_coverage( Since the tissue is stained with the **H&E protocol**, we used the `RGB2HER` color conversion with the corresponding index of the residual channel. The threshold value is based on the currently recommended value in the function's documentation. Finally, the nucleus area is manually approximated from the input image. -After obtaining the results, we can save the binary artifact mask and print the computed `coverage` number: +After obtaining the results, we can save the binary artifact mask and print the number of examined and flagged pixels: -```python linenums="19" -mask = Image.fromarray(255 * artifacts["coverage_mask"].astype(np.uint8)) +```python linenums="18" +mask = Image.fromarray(255 * artifacts["artifacts_per_pixel"].astype(np.uint8)) mask.save("residual_mask.png") -print("Coverage Number:", artifacts["coverage"]) +print(f"Number of flagged pixels: {artifacts['number_of_flagged_pixels']}") +print(f"Number of examined pixels: {artifacts['number_of_examined_pixels']}") ``` -The computed mask is returned as a binary image. Therefore, the computed values need to be scaled into the [0, 255] range before visualization. +The computed mask is returned as a binary image. Therefore, the computed values need to be scaled into the `[0, 255]` range before visualization. #### Results -In our example, the `coverage` number came out to be `0.0766`, meaning that little more than 7% of the image's foreground area is covered by the artifact. + +In our example, 17900 out of the 233744 examined pixels were labeled as artifacts, +meaning that **little more than 7% of the image's foreground area** is covered by artifacts. Finally, the computed artifact mask looks like this: ![The generated mask](data/residual_mask.png) -### Detecting blurry areas +### Detecting Blurry Areas In this example, we will work with the following images: -| ![](data/blur/blur_A.png) | ![](data/blur/blur_B.png) | ![](data/blur/blur_C.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image | Partially blurred image | Blurred image | +| ![Focused Image](data/blur/blur_A.png) | ![Partially Blurred Image](data/blur/blur_B.png) | ![Blurred Image](data/blur/blur_C.png) | +|:--------------------------------------:|:------------------------------------------------:|:--------------------------------------:| +| Focused Image | Partially Blurred Image | Blurred Image | -#### Sample code First, we need to import the necessary functionality and load the images into numpy arrays: ```python linenums="1" import numpy as np -from PIL import Image - -# optional, tissue mask will be calculated inside the blur_score function if not provided as an argument import pyvips +from PIL import Image from rationai.masks import tissue_mask +from rationai.qc import blur_score_piqe img_a = np.asarray(Image.open("blur_A.png").convert("RGB")) # Focused image @@ -90,32 +96,34 @@ img_c = np.asarray(Image.open("blur_C.png").convert("RGB")) # Blurred image Next, we will need to prepare the arguments for the [blur_score_piqe](../api/blur/blur-score-piqe.md) function and call it on the input images: -```python linenums="9" +```python linenums="12" pixel_size = 0.44 # pixel size of input images in micrometers # blur score without tissue mask blur_score_a = blur_score_piqe(img_a, pixel_size) blur_score_b = blur_score_piqe(img_b, pixel_size) -# blur score with tissue mask -tissue_mask = tissue_mask( +tissue_mask_img = tissue_mask( pyvips.Image.new_from_array(img_c), mpp=pixel_size - ).numpy() +).numpy() -tissue_mask = (tissue_mask > 0).astype(int) # binarize mask +tissue_mask_img = (tissue_mask_img > 0).astype(int) # binarize mask -blur_score_c = blur_score_piqe(img_c, pixel_size, tissue_mask ) +# blur score with tissue mask +blur_score_c = blur_score_piqe(img_c, pixel_size, tissue_mask_img) ``` -The `pixel_size` parameter is used to calculate the kernel size for median filter that is used during the computation. Function gives most accurate results on images with pixel size around 0.44 micrometers. The `tissue_mask` param is optional and will be calculated inside the function if not present, but can be provided by the user to avoid unnecessary computation. +The `pixel_size` parameter is used to calculate the kernel size for median filter that is used during the computation. +The function gives most accurate results on images with pixel size around 0.44 micrometers. +The `tissue_mask` param is optional and will be calculated inside the function if not present, but can be provided by the user to avoid unnecessary computation. -blur function outputs 2 masks - blur_score_coverage and blur_score_per_pixel. -blur_score_per_pixel is a binary mask marking the areas of the image that are considered blurred, while blur_score_coverage provides a blur coverage score for the whole image indicating the degree of blur. +The blur function outputs 2 masks - `blur_score_coverage` and `blur_score_per_pixel`. +`blur_score_per_pixel` is a binary mask marking the areas of the image that are considered blurred, while `blur_score_coverage` provides a blur coverage score for the whole image indicating the degree of blur. -After obtaining the results, we can save the computated blur score masks. -Since blur_score_coverage is homogeneous across the image, we can just print out any value from the score mask for quick inspection: +After obtaining the results, we can save the computed blur score masks. +Since `blur_score_coverage` is homogeneous across the image, we can just print out any value from the score mask for quick inspection: -```python linenums="18" +```python linenums="29" print(blur_score_a["blur_score_coverage"][0][0]) @@ -144,33 +152,32 @@ mask.save("blur_score_c_pixel.png") ``` #### Results + Coverage scores range from 0 to 1, where 0 represents the best focus and 1 represents complete blur. -| Score | Focus Level | -|--------------|---------------------------| +| Score | Focus Level | +|--------------|------------------------------| | 0 - 0.1 | Perfect/almost perfect focus | | 0.1 - 0.3 | Slightly/partially blurred | | 0.3 - 0.7 | Visibly blurred | | 0.7 - 1 | Severely blurred | - In our example, the focus scores and masks look like this: -| ![](data/blur/blur_score_a.png) | ![](data/blur/blur_score_b.png) | ![](data/blur/blur_score_c.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image, Score ~0.005 | Partially blurred image, Score ~0.488 | Blurred image, Score ~0.985 | - +| ![Focused Image](data/blur/blur_score_a.png) | ![Partially Blurred Image](data/blur/blur_score_b.png) | ![Blurred Image](data/blur/blur_score_c.png) | +|:--------------------------------------------:|:------------------------------------------------------:|:--------------------------------------------:| +| Focused Image, Score ~0.005 | Partially Blurred Image, Score ~0.488 | Blurred Image, Score ~0.985 | Per-pixel mask is binary mask composed of 16x16 px blocks marking blurred areas. Background pixels and focused blocks are marked as 0, while blurred blocks are marked as 1. -| ![](data/blur/blur_score_a_pixel.png) | ![](data/blur/blur_score_b_pixel.png) | ![](data/blur/blur_score_c_pixel.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image | Partially blurred image | Blurred image | +| ![Focused Image](data/blur/blur_score_a_pixel.png) | ![Partially Blurred Image](data/blur/blur_score_b_pixel.png) | ![Blurred Image](data/blur/blur_score_c_pixel.png) | +|:--------------------------------------------------:|:------------------------------------------------------------:|:--------------------------------------------------:| +| Focused Image | Partially Blurred Image | Blurred Image | Please note that blocks of the per-pixel mask may be 1 not only for globally blurred regions, but also for locally defocused or sparse areas (e.g. tissue gaps or empty regions). This means a value of 1 does not always imply a complete blur. -### Detecting folded areas +### Detecting Folded Areas In this example, we will work with the following images: @@ -182,46 +189,54 @@ The local area of the tile. ![Local area around tile](data/fold_area.png) -The local area is an optional image. It increases the detection rate of particularly large folds. A suggested size is (3 * width, 3 * height) of investigated image +The local area is an optional image. It increases the detection rate of particularly large folds. A suggested size is `(3 * width, 3 * height)` of the investigated image. + +#### Sample Code -#### Sample code First, we need to import the necessary functionality and load the images into numpy arrays: + ```python linenums="1" import numpy as np -from PIL import Image - import pyvips +from PIL import Image from rationai.masks import tissue_mask +from rationai.qc import folding -img = np.asarray(Image.open("fold.png").conver("RGB")) # Investigated image -local_area_image =img_area = np.asarray(Image.open("fold_area.png").convert("RGB")) # Local area of investigated image +mpp = 1.76 +img = np.asarray(Image.open("fold.png").convert("RGB")) # Investigated image +local_area_image = np.asarray(Image.open("fold_area.png").convert("RGB")) # Local area of investigated image ``` Now we can calculate the tissue masks: -```python linenums="1" + +```python linenums="12" img_mask = tissue_mask( - pyvips.Image.new_from_array(img), mpp=pixel_size).numpy() > 0 + pyvips.Image.new_from_array(img), mpp=mpp +).numpy() > 0 img_area_mask = tissue_mask( - pyvips.Image.new_from_array(local_area_img), mpp=pixel_size).numpy() > 0 + pyvips.Image.new_from_array(local_area_image), mpp=mpp +).numpy() > 0 ``` Now we have all the arguments prepared. The folding function can be called: -```python linenums="1" + +```python linenums="20" artifacts = folding(img=img, - mpp=1.76, + mpp=mpp, hematoxylin_eosin_stained=True, tissue_mask=img_mask, - local_tiles=local_area_img, + local_tiles=local_area_image, local_mask=img_area_mask) ``` -The level_downsample argument is the downsample rate between the highest resolution level and the level from which the image was taken. nuclues_diameter_at_base_level specifies the nucleus diameter at the highest resolution level (it can be easily measured when browsing the WSI). #### Results -To recover the results, one need to access the dictionary `artifacts`. -```python linenums="1" -mask = Image.fromarray(255 * artifacts["folding"].astype(np.uint8)) +To recover the results, one needs to access the `artifacts` dictionary. + +```python linenums="27" +mask = Image.fromarray(255 * artifacts["folding_per_pixel"].astype(np.uint8)) ``` + ![The resulting mask](data/fold_detection.png) diff --git a/docs/index.md b/docs/index.md index 463c077..a7d39a2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -3,4 +3,4 @@ This repository contains a selection of image-processing routines, **forming a foundation for QC methods** developed at RationAI. 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{ name = "Tomáš Balák", email = "525126@mail.muni.cz" }, { name = "Samuel Tichý", email = "524626@mail.muni.cz" }, + { name = "Karel Štěpka", email = "chuckie@mail.muni.cz" }, ] requires-python = ">=3.11" readme = "README.md" license = { file = "LICENSE" } + dependencies = [ "scikit-image>=0.24.0", "pyvips>=2.2.3", - "rationai-staining @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/staining.git", "opencv-python>=4.10.0.84", "rationai-masks @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/masks.git", + "rationai-staining @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/staining.git", ] -[tool.pdm.dev-dependencies] +[dependency-groups] dev = ["mypy", "pre-commit", "ruff"] test = ["pytest", "pytest-cov"] -docs = ["mkdocs-material>=9.5.32", "mkdocstrings[python]>=0.25.2"] - -[tool.pdm.scripts] -l = { composite = ["lint", "format", "mypy"] } -test = "pytest tests" -lint = "ruff check --fix" -format = "ruff format" -mypy = "mypy ." -docs = "mkdocs serve" -post_install = { composite = [ - "pre-commit autoupdate", - "pre-commit install", - "pre-commit install --hook-type commit-msg", -] } +docs = [ + "mkdocs-material>=9.5.32", + "mkdocstrings[python]>=0.25.2", + "pymdown-extensions>=10.20.1", +] diff --git a/rationai/qc/blur/blur_score_laplacian.py b/rationai/qc/blur/blur_score_laplacian.py index 34b779d..00a3fc1 100644 --- a/rationai/qc/blur/blur_score_laplacian.py +++ b/rationai/qc/blur/blur_score_laplacian.py @@ -1,7 +1,8 @@ import numpy as np +from rationai.staining import StandardConversions, convert_color from skimage.color import rgb2gray from skimage.filters import laplace, threshold_otsu -from skimage.morphology import binary_dilation, binary_erosion +from skimage.morphology import dilation, erosion from rationai.qc.blur.utils import ( get_coverage_mask, @@ -9,8 +10,6 @@ simple_foreground_mask, ) from rationai.qc.typing import BinaryMask, BlurScore, RGBImage -from rationai.staining import convert_color -from rationai.staining.convert_color import ColorConversion def blur_score_laplacian( @@ -46,10 +45,12 @@ def blur_score_laplacian( Note: The returned dictionary contains the following values: - | Key | Description | - |-----------------------|---------------------------------| - | `blur_score_per_pixel`| Binary mask of the blur score. | - | `blur_score_coverage` | Coverage mask of the blur score.| + | Key | Description | + |-----------------------------|-------------------------------------------------------| + | `blur_score_per_pixel` | Binary mask of the blur score. | + | `blur_score_coverage` | Coverage mask of the blur score. | + | `number_of_examined_pixels` | Number of pixels that were evaluated by the function. | + | `number_of_flagged_pixels` | Number of pixels labeled as artifacts. | Examples: ```python @@ -74,7 +75,7 @@ def blur_score_laplacian( if foreground_mask is None: foreground_mask = simple_foreground_mask(grayscale_img) - hematoxylin, _, _ = convert_color(img, ColorConversion.RGB2HER) + hematoxylin, _, _ = convert_color(img, StandardConversions.RGB2HER) hematoxylin_threshold = threshold_otsu(hematoxylin) hematoxylin_mask = hematoxylin > hematoxylin_threshold @@ -85,20 +86,20 @@ def blur_score_laplacian( # 2 pixels wide border is removed from the pooling mask for each hematoxylin nucleus # to ignore gradient values on the edges of the nuclei pooling_mask = foreground_mask * ~( - binary_dilation(hematoxylin_mask, footprint=footprint) - ^ binary_erosion(hematoxylin_mask, footprint=footprint) + dilation(hematoxylin_mask, footprint=footprint) + ^ erosion(hematoxylin_mask, footprint=footprint) ) blur_score = np.abs(gradient) - blur_score_per_pixel = masked_average_pooling(blur_score, pooling_mask) + blur_score_pooled = masked_average_pooling(blur_score, pooling_mask) # Threshold was set to 5 based on empirical testing # Can be adjusted based on the desired sensitivity # Higher threshold means more pixels are considered blurred - blur_score_per_pixel = blur_score_per_pixel < threshold + blur_score_per_pixel = blur_score_pooled < threshold # activity_mask is multiplied by the foreground mask to nullify background pixels - blur_score_per_pixel = blur_score_per_pixel * foreground_mask + blur_score_per_pixel *= foreground_mask return { "blur_score_per_pixel": blur_score_per_pixel, @@ -107,4 +108,6 @@ def blur_score_laplacian( detection_mask=blur_score_per_pixel, foreground_mask=foreground_mask, ), + "number_of_examined_pixels": int(np.count_nonzero(foreground_mask)), + "number_of_flagged_pixels": int(np.count_nonzero(blur_score_per_pixel)), } diff --git a/rationai/qc/blur/blur_score_piqe.py b/rationai/qc/blur/blur_score_piqe.py index c9f4958..f20fe49 100644 --- a/rationai/qc/blur/blur_score_piqe.py +++ b/rationai/qc/blur/blur_score_piqe.py @@ -33,10 +33,12 @@ def blur_score_piqe( Note: The returned dictionary contains the following values: - | Key | Description | - |-----------------------|---------------------------------| - | `blur_score_per_pixel`| Binary mask of the blur score. | - | `blur_score_coverage` | Coverage mask of the blur score.| + | Key | Description | + |-----------------------------|-------------------------------------------------------| + | `blur_score_per_pixel` | Binary mask of the blur score. | + | `blur_score_coverage` | Coverage mask of the blur score. | + | `number_of_examined_pixels` | Number of pixels that were evaluated by the function. | + | `number_of_flagged_pixels` | Number of pixels labeled as artifacts. | Examples: ```python @@ -82,14 +84,14 @@ def blur_score_piqe( if foreground_mask is None: foreground_mask = simple_foreground_mask(grayscale_img) - # activity_mask is inverted, blurred areas and empty areas (background) are set to 1 - activity_mask = ~(activity_mask > 0) - # activity_mask is multiplied by the foreground mask to nullify background pixels - activity_mask = activity_mask * foreground_mask + # Invert and restrict the mask to foreground + activity_mask = foreground_mask * ~(activity_mask > 0) return { - "blur_score_per_pixel": activity_mask > 0, + "blur_score_per_pixel": activity_mask, "blur_score_coverage": get_coverage_mask( grayscale_img, activity_mask, foreground_mask ), + "number_of_examined_pixels": int(np.count_nonzero(foreground_mask)), + "number_of_flagged_pixels": int(np.count_nonzero(activity_mask)), } diff --git a/rationai/qc/blur/blur_score_roberts.py b/rationai/qc/blur/blur_score_roberts.py index 8cfd728..781edc8 100644 --- a/rationai/qc/blur/blur_score_roberts.py +++ b/rationai/qc/blur/blur_score_roberts.py @@ -40,10 +40,12 @@ def blur_score_roberts( Note: The returned dictionary contains the following values: - | Key | Description | - |-----------------------|---------------------------------| - | `blur_score_per_pixel`| Binary mask of the blur score. | - | `blur_score_coverage` | Coverage mask of the blur score.| + | Key | Description | + |-----------------------------|-------------------------------------------------------| + | `blur_score_per_pixel` | Binary mask of the blur score. | + | `blur_score_coverage` | Coverage mask of the blur score. | + | `number_of_examined_pixels` | Number of pixels that were evaluated by the function. | + | `number_of_flagged_pixels` | Number of pixels labeled as artifacts. | Examples: ```python @@ -72,17 +74,17 @@ def blur_score_roberts( gaussian_gradient = roberts(gaussian(grayscale_img, sigma=1)) blur_score = np.abs(gradient - gaussian_gradient) - blur_score_per_pixel = masked_average_pooling( + blur_score_pooled = masked_average_pooling( arr=blur_score, foreground_mask=foreground_mask ) # Threshold was set to 2 based on empirical testing # Can be adjusted based on the desired sensitivity # Higher threshold means more pixels are considered blurred - blur_score_per_pixel = blur_score_per_pixel < threshold + blur_score_per_pixel = blur_score_pooled < threshold # activity_mask is multiplied by the foreground mask to nullify background pixels - blur_score_per_pixel = blur_score_per_pixel * foreground_mask + blur_score_per_pixel *= foreground_mask return { "blur_score_per_pixel": blur_score_per_pixel, @@ -91,4 +93,6 @@ def blur_score_roberts( detection_mask=blur_score_per_pixel, foreground_mask=foreground_mask, ), + "number_of_examined_pixels": int(np.count_nonzero(foreground_mask)), + "number_of_flagged_pixels": int(np.count_nonzero(blur_score_per_pixel)), } diff --git a/rationai/qc/blur/utils.py b/rationai/qc/blur/utils.py index 3232ab4..d4a2950 100644 --- a/rationai/qc/blur/utils.py +++ b/rationai/qc/blur/utils.py @@ -1,10 +1,15 @@ +from typing import Any + import numpy as np -from skimage.morphology import area_opening, binary_erosion +from numpy.typing import NDArray +from skimage.morphology import area_opening, erosion -from rationai.qc.typing import BinaryMask, GrayScaleImage, NDArray +from rationai.qc.typing import BinaryMask, FloatingPointImage -def masked_average_pooling(arr, foreground_mask): +def masked_average_pooling( + arr: FloatingPointImage, foreground_mask: BinaryMask +) -> FloatingPointImage: """Perform masked average pooling, creating mask of 16x16 blocks. For images with dimensions not divisible by 16, the function pads the image @@ -55,14 +60,14 @@ def masked_average_pooling(arr, foreground_mask): def get_coverage_mask( - grayscale_img: GrayScaleImage, + tissue_img: NDArray[Any], detection_mask: BinaryMask, foreground_mask: BinaryMask, -) -> NDArray[np.float64]: - """Creates a blur coverage mask based on the grayscale image and binary mask of blur detections. +) -> FloatingPointImage: + """Creates a blur coverage mask based on the tissue image and binary mask of blur detections. Args: - grayscale_img: Grayscale image of the tissue. + tissue_img: Image of the tissue. detection_mask: Binary image of the blur detection. foreground_mask: Binary mask of the tissue, where 1 represents tissue and 0 represents background. @@ -72,7 +77,7 @@ def get_coverage_mask( """ foreground_area = np.count_nonzero(foreground_mask) - coverage_mask = np.ones_like(grayscale_img).astype(float) + coverage_mask = np.ones_like(tissue_img).astype(np.float64) coverage = ( (np.sum(detection_mask * foreground_mask) / foreground_area) if foreground_area > 0 @@ -84,7 +89,7 @@ def get_coverage_mask( def simple_foreground_mask( - grayscale_img: NDArray[np.float64], + grayscale_img: FloatingPointImage, threshold: float = 0.92, # ~235/255 ) -> BinaryMask: """Creates a simple foreground mask using thresholding and morphological operations. @@ -101,6 +106,6 @@ def simple_foreground_mask( foreground_mask = grayscale_img < threshold foreground_mask = area_opening(foreground_mask) - foreground_mask = binary_erosion(foreground_mask, footprint=np.ones((5, 5))) + foreground_mask = erosion(foreground_mask, footprint=np.ones((5, 5))) return foreground_mask diff --git a/rationai/qc/folding/folding.py b/rationai/qc/folding/folding.py index 65d2320..8e330a3 100644 --- a/rationai/qc/folding/folding.py +++ b/rationai/qc/folding/folding.py @@ -1,34 +1,45 @@ import numpy as np from numpy.ma import MaskedArray from numpy.typing import NDArray +from rationai.staining import StandardConversions, convert_color from skimage.color import rgb2hsv from skimage.filters import threshold_yen -from skimage.morphology import binary_opening, disk, reconstruction +from skimage.morphology import disk, opening, reconstruction from rationai.qc.typing import BinaryMask, FoldArtifacts, RGBImage -from rationai.staining import ColorConversion, convert_color def _get_threshold( - img: NDArray[np.float32], - mask: BinaryMask, # type: ignore[PGH003] - local_tiles: NDArray[np.float32] | None = None, - local_mask: NDArray[bool] | None = None, # type: ignore[PGH003] + img: NDArray[np.float64], + mask: BinaryMask, + local_tiles: NDArray[np.float64] | None = None, + local_mask: BinaryMask | None = None, ) -> float: """Calculates adequate threshold from given images. Args: - img : A given channel of an image. - mask : Background mask of an image. - local_tiles : Optional n*n tiles in local neighbourhood of tile. Defaults to None. - local_mask : Optional n*n background mask of local_tiles. Defaults to None. + img: A given channel of an image. + mask: Background mask of an image. + local_tiles: Optional n*n tiles in local neighbourhood of tile. Defaults to None. + local_mask: Optional n*n background mask of local_tiles. Defaults to None. Returns: Value which can be used to threshold the image. """ - if local_tiles is None: - return threshold_yen(MaskedArray(img, ~mask).compressed()) - return threshold_yen(MaskedArray(local_tiles, ~local_mask).compressed()) + if local_tiles is not None and local_mask is not None: + local_values = MaskedArray(local_tiles, ~local_mask).compressed() + + if local_values.size > 0: + return threshold_yen(local_values) + else: + return 1.0 + + values = MaskedArray(img, ~mask).compressed() + + if values.size == 0: + return 1.0 + + return threshold_yen(values) def folding( @@ -60,12 +71,14 @@ def folding( Note: The returned dictionary contains the following values: - | Key | Description | - |---------------------------|---------------------------------------| - | `folding` | Binary mask of the detected folds. | - | `thresholded_saturation` | | - | `thresholded_value` | | - | `thresholded_eosin` | | + | Key | Description | + |-----------------------------|-------------------------------------------------------| + | `folding_per_pixel` | Binary mask of the detected folds. | + | `thresholded_saturation` | | + | `thresholded_value` | | + | `thresholded_eosin` | | + | `number_of_examined_pixels` | Number of pixels that were evaluated by the function. | + | `number_of_flagged_pixels` | Number of pixels labeled as artifacts. | Examples: ```python @@ -79,7 +92,7 @@ def folding( result = folding(img, 8, False, tissue_mask) - mask = result["folding"] # Contains values 0 and 1 + mask = result["folding_per_pixel"] # Contains values 0 and 1 ``` """ @@ -99,15 +112,15 @@ def folding( ) local_value_channel = 1 - local_value_channel if hematoxylin_eosin_stained: - _, eosin_channel, _ = convert_color(tile, ColorConversion.RGB2HER) + _, eosin_channel, _ = convert_color(tile, StandardConversions.RGB2HER) if local_tiles is not None: _, local_eosin_channel, _ = convert_color( - local_tiles, ColorConversion.RGB2HER + local_tiles, StandardConversions.RGB2HER ) else: eosin_channel = np.ones_like(tissue_mask) if local_tiles is not None: - local_eosin_channel = np.ones_like(local_tiles) + local_eosin_channel = np.ones_like(local_tiles, dtype=np.float64) inverted_value_channel = 1 - value_channel @@ -117,7 +130,7 @@ def folding( saturation_threshold = _get_threshold( saturation_channel, tissue_mask, local_saturation_channel, local_mask ) - if hematoxylin_eosin_stained: + if hematoxylin_eosin_stained and eosin_channel is not None: eosin_threshold = _get_threshold( eosin_channel, tissue_mask, local_eosin_channel, local_mask ) @@ -135,24 +148,21 @@ def folding( ): thresholded_value = np.zeros(tile.shape) - folding_test_markers = binary_opening( + folding_test_markers = opening( thresholded_eosin & thresholded_saturation & thresholded_value, disk(cell_nucleus_size // (mpp)), ) if hematoxylin_eosin_stained: folding_test = reconstruction(folding_test_markers, thresholded_eosin) - result: FoldArtifacts = { - "folding": folding_test, - "thresholded_saturation": thresholded_saturation, - "thresholded_eosin": thresholded_eosin, - "thresholded_value": thresholded_value, - } - return result - result: FoldArtifacts = { - "folding": folding_test, + else: + folding_test = folding_test_markers + + return { + "folding_per_pixel": folding_test, "thresholded_saturation": thresholded_saturation, "thresholded_eosin": thresholded_eosin, "thresholded_value": thresholded_value, + "number_of_examined_pixels": int(np.count_nonzero(tissue_mask)), + "number_of_flagged_pixels": int(np.count_nonzero(folding_test)), } - return result diff --git a/rationai/qc/residual_artifacts/residual_artifacts_and_coverage.py b/rationai/qc/residual_artifacts/residual_artifacts_and_coverage.py index d87d971..a7e431c 100644 --- a/rationai/qc/residual_artifacts/residual_artifacts_and_coverage.py +++ b/rationai/qc/residual_artifacts/residual_artifacts_and_coverage.py @@ -1,8 +1,8 @@ import numpy as np +from rationai.staining import ColorConversion, convert_color from skimage.morphology import area_opening from rationai.qc.typing import BinaryMask, ResidualArtifacts, RGBImage -from rationai.staining import ColorConversion, convert_color def _get_foreground_mask( @@ -39,7 +39,7 @@ def _get_debris_coverage( nucleus_area: float, res_index: int, threshold: float, -) -> tuple[float, BinaryMask]: +) -> tuple[int, int, BinaryMask]: """Calculates the percentage of the foreground debris coverage. Args: @@ -55,8 +55,8 @@ def _get_debris_coverage( slightly higher values could also provide reasonable results. Returns: - Number of pixels marked as artifact compared to number - of foreground pixels and thresholded residual channel. + Number of foreground pixels examined, number of foreground pixels + marked as artifact, and the thresholded residual channel. """ residual = np.asarray( convert_color(tile=img, conversion=conv)[res_index], dtype=np.float64 @@ -73,11 +73,9 @@ def _get_debris_coverage( residual_mask *= foreground_mask if foreground_area <= 0: - return 0.0, residual_mask + return 0, 0, residual_mask - return float( - round(np.count_nonzero(residual_mask) / foreground_area, 4) - ), residual_mask + return int(foreground_area), int(np.count_nonzero(residual_mask)), residual_mask def residual_artifacts_and_coverage( @@ -102,35 +100,37 @@ def residual_artifacts_and_coverage( slightly higher values could also provide reasonable results. Returns: - Dictionary with a binary coverage mask and a coverage number. + Dictionary with a number of examined pixels, number of flagged pixels, + and a binary mask of artifacts. Note: The returned dictionary contains the following values: - | Key | Description | - |-------------------|-----------------------------------------------------------------------------------------------| - | `coverage_mask` | Binary mask of the detected residual artifacts. | - | `coverage` | A number that states what portion of the image's foreground area is covered by the artifacts. | + | Key | Description | + |-----------------------------|-------------------------------------------------------| + | `artifacts_per_pixel` | Binary mask of the detected residual artifacts. | + | `number_of_examined_pixels` | Number of pixels that were evaluated by the function. | + | `number_of_flagged_pixels` | Number of pixels labeled as artifacts. | Examples: ```python from skimage.data import immunohistochemistry from rationai.qc import residual_artifacts_and_coverage - from rationai.staining import ColorConversion + from rationai.staining import StandardConversions img = immunohistochemistry() result = residual_artifacts_and_coverage( - img, ColorConversion.RGB2HDR, nucleus_area=150, res_index=2, threshold=0.013 + img, StandardConversions.RGB2HDR, nucleus_area=150, res_index=2, threshold=0.013 ) - mask = result["coverage_mask"] # Contains values 0 and 1 - print(result["coverage"]) + mask = result["artifacts_per_pixel"] # Contains values 0 and 1 + print(result["number_of_flagged_pixels"], result["number_of_examined_pixels"]) ``` """ - coverage, cov_heatmap = _get_debris_coverage( + num_examined, num_flagged, cov_heatmap = _get_debris_coverage( img=img, conv=conversion, nucleus_area=nucleus_area, @@ -139,8 +139,9 @@ def residual_artifacts_and_coverage( ) result: ResidualArtifacts = { - "coverage_mask": cov_heatmap, - "coverage": coverage, + "artifacts_per_pixel": cov_heatmap, + "number_of_examined_pixels": num_examined, + "number_of_flagged_pixels": num_flagged, } return result diff --git a/rationai/qc/staining/color_difference.py b/rationai/qc/staining/color_difference.py index f13c8f5..14acfff 100644 --- a/rationai/qc/staining/color_difference.py +++ b/rationai/qc/staining/color_difference.py @@ -1,9 +1,9 @@ import numpy as np from numpy.typing import NDArray +from rationai.staining import ColorConversion, ConversionDirection from skimage.color import deltaE_cie76, deltaE_ciede94, deltaE_ciede2000, rgb2lab from rationai.qc.typing import Stain -from rationai.staining import ColorConversion, ConversionType def _stain2rgb(stain: Stain) -> NDArray[np.float64]: @@ -13,10 +13,10 @@ def _stain2rgb(stain: Stain) -> NDArray[np.float64]: def reference_stain(conversion: ColorConversion, index: int) -> Stain: """Return specified stain vector for a given conversion.""" - if conversion.conv_type == ConversionType.RGB2STAIN: + if conversion.direction == ConversionDirection.RGB2STAIN: conversion = conversion.inverse - return np.array(conversion.value[0][index], dtype=np.float64) + return conversion.matrix[index] def color_difference( diff --git a/rationai/qc/staining/dominant_stains.py b/rationai/qc/staining/dominant_stains.py index 839ae7c..614baac 100644 --- a/rationai/qc/staining/dominant_stains.py +++ b/rationai/qc/staining/dominant_stains.py @@ -1,6 +1,7 @@ -from rationai.qc.typing import DominantStains, RGBImage from rationai.staining import estimate_stain_vectors +from rationai.qc.typing import DominantStains, RGBImage + def dominant_stains( img: RGBImage, i0: int = 240, alpha: int = 1, beta: float = 0.15 diff --git a/rationai/qc/staining/staining_difference.py b/rationai/qc/staining/staining_difference.py index 3195b31..45c3574 100644 --- a/rationai/qc/staining/staining_difference.py +++ b/rationai/qc/staining/staining_difference.py @@ -1,10 +1,11 @@ +from rationai.staining import ColorConversion + from rationai.qc.staining.color_difference import ( closest_difference, color_difference, reference_stain, ) from rationai.qc.typing import Stain, StainingDifference -from rationai.staining import ColorConversion def staining_difference( @@ -81,7 +82,7 @@ def staining_difference( from skimage.data import immunohistochemistry from rationai.qc.staining import dominant_stains, staining_difference - from rationai.staining import ColorConversion + from rationai.staining import StandardConversions img = immunohistochemistry() @@ -90,7 +91,7 @@ def staining_difference( stain1, stain2 = stains["stain1"], stains["stain2"] result = staining_difference( - ColorConversion.RGB2HDR, stain1, stain2, 33, 25, "ciede_2000", "cie_76" + StandardConversions.RGB2HDR, stain1, stain2, 33, 25, "ciede_2000", "cie_76" ) print(result["correct_staining"]) print(result["stain_diff1"], result["stain_diff2"]) diff --git a/rationai/qc/typing.py b/rationai/qc/typing.py index 42cc42f..c1c8fdb 100644 --- a/rationai/qc/typing.py +++ b/rationai/qc/typing.py @@ -19,6 +19,11 @@ Single channel grayscale image represented by a numpy array. """ +FloatingPointImage: TypeAlias = NDArray[np.float64] +""" +Floating point image represented by a numpy array. +""" + Stain: TypeAlias = NDArray[np.float64] """ Single stain vector (of 3 values) represented by a numpy array. @@ -51,16 +56,19 @@ class DominantStains(TypedDict): class ResidualArtifacts(TypedDict): - """Dictionary containing a coverage mask and a coverage number.""" + """Dictionary containing an artifact mask and a number of examined and flagged pixels.""" - coverage_mask: BinaryMask + artifacts_per_pixel: BinaryMask """Binary mask of the detected residual artifacts.""" - coverage: float - """A number that states what portion of the image's foreground area - is covered by the artifacts. + number_of_examined_pixels: int + """Number of pixels that were examined by the function and could + be theoretically marked as artifacts. """ + number_of_flagged_pixels: int + """Number of pixels that were labeled as artifacts by the function.""" + class StainingDifference(TypedDict): """Dictionary with color differences and a correct staining verdict.""" @@ -83,23 +91,45 @@ class BlurScore(TypedDict): blur_score_per_pixel: BinaryMask """Binary mask composed of 16x16 px blocks marking blurred area""" - blur_score_coverage: NDArray[np.float64] + blur_score_coverage: FloatingPointImage """Coverage mask of the blur score. Coverage ranges from 0.0 to 1.0. 0.0 - no blur, 1.0 - full blur """ + number_of_examined_pixels: int + """Number of pixels that were examined by the function and could + be theoretically marked as artifacts. + """ + + number_of_flagged_pixels: int + """Number of pixels that were labeled as artifacts by the function.""" + class FoldArtifacts(TypedDict): """Dictionary containing the fold detection mask and intermediate results.""" - folding: BinaryMask + folding_per_pixel: BinaryMask """Mask of the fold detection""" thresholded_saturation: BinaryMask - """Thresholded saturation channel. Intermediate result of folding detection. Can be used for debugging.""" + """Thresholded saturation channel. Intermediate result of folding detection. + Can be used for debugging. + """ thresholded_value: BinaryMask - """Thresholded value channel.Intermediate result of folding detection. Can be used for debugging.""" + """Thresholded value channel. Intermediate result of folding detection. + Can be used for debugging. + """ thresholded_eosin: BinaryMask - """Thresholded eosin channel.Intermediate result of folding detection. Can be used for debugging.""" + """Thresholded eosin channel. Intermediate result of folding detection. + Can be used for debugging. + """ + + number_of_examined_pixels: int + """Number of pixels that were examined by the function and could + be theoretically marked as artifacts. + """ + + number_of_flagged_pixels: int + """Number of pixels that were labeled as artifacts by the function.""" diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..445d213 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2501 @@ +version = 1 +revision = 2 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform 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