None
None
- Fix bug related to reading DICOM files
- Restricts wsidicom version to <0.7.0 for compatibility
- Adds NuClick architecture #449
- Adds Annotation Store Reader #476
- Adds DFBR method for registering pair of images #510
- Adds a sample SVS loading function
tiatoolbox.data.small_svs()
to the data module #517
- Simplifies example notebook for image reading for better readability
- Restricts Shapely version to <2.0.0 for compatibility
- Adds GitHub workflow for automatic generation of docker image
- Updates dependencies
- Updates bump2version config
- Enables flake8 E800 checks for commented codes.
- Fixes several errors generated by DeepSource.
- Prevent test dumping file to root
- Removes duplicate functions to generate parameterized test scenarios
- Adds an AnnotationTileGenerator and AnnotationRenderer which allows serving of tiles rendered directly from an annotation store.
- Adds DFBR registration model and jupyter notebook example
- Adds DICE metric
- Adds SCCNN architecture. [read the docs]
- Adds MapDe architecture. [read the docs]
- Adds support for reading MPP metadata from NGFF v0.4
- Adds enhancements to tiatoolbox.annotation.storage that are useful when using an AnnotationStore for visualization purposes.
- None
- Fixes colorbar_params #410
- Fixes Jupyter notebooks for better read the docs rendering
- Fixes typos, metadata and links
- Fixes nucleus_segmentor_engine for boundary artefacts
- Fixes the colorbar cropping in tests
- Adds citation in README.md and CITATION.cff to Nature Communications Medicine paper
- Fixes a bug #452 raised by @rogertrullo where only the numerator of the TIFF resolution tags was being read.
- Fixes HoVer-Net+ post-processing to be inline with original work.
- Fixes a bug where an exception would be raised if the OME XML is missing objective power.
- Uses Furo theme for readthedocs
- Replaces nbgallery and nbsphinx with myst-nb for jupyter notebook rendering
- Uses myst for markdown parsing
- Uses requirements.txt to define dependencies for requirements consistency
- Adds notebook AST pre-commit hook
- Adds check to validate python examples in the code
- Adds check to resolve imports
- Fixes an error in a docstring which triggered the failing test.
- Adds pre-commit hooks to format markdown and notebook markdown
- Adds pip install workflow to resolve dependencies when requirements file is updated
- Improves tiatoolbox import using LazyLoader
- None
- None
- Fixes issues with dependencies.
- Adds flask to dependencies.
- Fixes missing file in the python package.
- Clarifies help string for show-wsi option.
- Removes Travis CI.
- GitHub Actions will be used instead.
- Adds pre-commit hooks to check requirements consistency.
- Adds GitHub Action to resolve conda environment checks on Windows and Ubuntu.
- Adds support for Python 3.10
- Adds short description for IDARS algorithm #383
- Adds support for NGFF v0.4 OME-ZARR.
- Adds CLI for launching tile server.
- Renames
stainnorm_target()
function tostain_norm_target()
. - Removes
get_wsireader
- Replaces the custom PlattScaler in
tools/scale.py
with the regular Scikit-Learn LogisticRegression.
- Fixes bugs in UNET architecture.
- Number of channels in Batchnorm argument in the decoding path to match with the input channels.
- Padding
0
creates feature maps in the decoder part with the same size as encoder.
- Fixes linter issues and typos
- Fixes incorrect output with overlap in
predictor.merge_predictions()
andreturn_raw=True
- Thanks to @paulhacosta for raising #356, Fixed by #358.
- Fixes errors with JP2 read. Checks input path exists.
- Fixes errors with torch upgrade to 1.12.
- Adds pre-commit hooks for consistency across the repo.
- Sets up GitHub Actions Workflow.
- Travis CI will be removed in future release.
- Adds DICOM Support.
- Updates license to more permissive BSD 3-clause.
- Adds
micronet
model. - Improves support for
tiff
files.- Adds a check for tiles in a TIFF file when opening.
- Uses OpenSlide to read a TIFF if it has tiles instead of OpenCV (VirtualWSIReader).
- Adds a fallback to tifffile if it is tiled but openslide cannot read it (e.g. jp2k or jpegxl tiles).
- Adds support for multi-channel images (HxWxC).
- Fixes performance issues in
semantic_segmentor.py
.- Performance gain measurement: 21.67s (new) vs 45.564 (old) using a 4k x 4k WSI.
- External Contribution from @ByteHexler.
- Adds benchmark for Annotations Store.
- None
- Enhances the error messages to be more informative.
- Fixes Flake8 Errors, typos.
- Fixes patch predictor models based after fixing a typo.
- Bug fixes in Graph functions.
- Adds documentation for docker support.
- General tidying up of docstrings.
- Adds metrics to readthedocs/docstrings for pretrained models.
- Adds
pydicom
andwsidicom
as dependency. - Updates dependencies.
- Fixes Travis detection and makes improvements to run tests faster on Travis.
- Adds Dependabot to automatically update dependencies.
- Improves CLI definitions to make it easier to integrate new functions.
- Fixes compile options for test_annotation_stores.py
- Updates dependencies for conda recipe #262
- None
- Adds User Warning For Missing SQLite Functions
- Fixes Pixman version check errors
- Fixes empty query in instance segmentor
- Fixes flake8 linting issues and typos
- Conditional pytest.skipif to skip GPU tests on travis while running them locally or elsewhere
- Adds nucleus instance segmentation base class
- Adds HoVerNet architecture
- Adds multi-task segmentor HoVerNet+ model
- Adds IDaRS pipeline
- Adds SlideGraph pipeline
- Adds PCam patch classification models
- Adds support for stain augmentation feature
- Adds classes and functions under
tiatoolbox.tools.graph
to enable construction of graphs in a format which can be used with PyG (PyTorch Geometric). - Add classes which act as a mutable mapping (dictionary like) structure and enables efficient management of annotations. (#135)
- Adds example notebook for adding advanced models
- Adds classes which can generate zoomify tiles from a WSIReader object.
- Adds WSI viewer using Zoomify/WSIReader API (#212)
- Adds README to example page for clarity
- Adds support to override or specify mpp and power
- Replaces
models.controller
API withmodels.engine
- Replaces
CNNPatchPredictor
withPatchPredictor
- Fixes Fix
filter_coordinates
read wrong resolutions for patch extraction - For
PatchPredictor
ioconfig
will supersede everything- if
ioconfig
is not provided- If
model
is pretrained (defined inpretrained_model.yaml
)- Use the yaml ioconfig
- Any other input patch reading arguments will overwrite the yaml ioconfig (at the same keyword).
- If
model
is not defined, all input patch reading arguments must be provided else exception will be thrown.
- If
- Improves performance of mask based patch extraction
- Improve tests performance for Travis runs
- Adds feature detection mechanism to detect the platform and installed packages etc.
- On demand imports for some libraries for performance
- Improves performance of mask based patch extraction
- Adds
SemanticSegmentor
which is Predictor equivalent for semantic segmentation. - Add
TIFFWSIReader
class to support OMETiff reading. - Adds
FeatureExtractor
API to controller. - Adds WSI Serialization Dataset which support changing parallel workers on the fly. This would reduce the time spent to create new worker for every WSI/Tile (costly).
- Adds IOState data class to contain IO information for loading input to model and assembling model output back to WSI/Tile.
- Minor updates for
get_coordinates
to pave the way for getting patch IO for segmentation. - Migrates old code to new variable names (patch extraction, patch wsi model).
- Change in API from
pretrained_weight
topretrained_weights
. - Adds cli for semantic segmentation.
- Update python notebooks to add
read_rect
andread_bounds
examples withmpp
read.
- Adds
WSIReader.open
.get_wsireader
will deprecate in the next release. Please useWSIReader.open
instead. - CLI is now POSIX compatible
- Replaces underscores in variable names with hyphens
- Models API updated to use
pretrained_weights
instead ofpretrained_weight
. - Move string_to_tuple to tiatoolbox/utils/misc.py
- Fixes README git clone instructions.
- Fixes stain normalisation due to changes in sklearn.
- Fixes a test in tests/test_slide_info
- Fixes readthedocs documentation issues
- Adds dependencies for tiffile, imagecodecs, zarr.
- Adds more stringent pre-commit checks
- Moved local test files into
tiatoolbox/data
. - Fixed
Manifest.ini
and addedtiatoolbox/data
. This means that this directory will be downloaded with the package. - Using
pkg_resources
to properly load bundled resources (e.g.target_image.png
) intiatoolbox.data
. - Removed duplicate code in
conftest.py
for downloading remote files. This is now intiatoolbox.data._fetch_remote_file
. - Fixes errors raised by new flake8 rules.
- Remove leading underscores from fixtures.
- Rename some remote sample files to make more sense.
- Moves all cli commands/options from cli.py to cli_commands to make it clean and easier to add new commands
- Removes redundant tests
- Updates to new GitHub organisation name in the repo
- Fixes related links
- Drops support for python 3.6
- Update minimum requirement to python 3.7
- Adds support for python 3.9
- Adds
models
base to the repository. Currently, PyTorch models are supported. New custom models can be added. The tiatoolbox also supports using custom weights to pre-existing built-in models.- Adds
classification
package and CNNPatchPredictor which takes predefined model architecture and pre-trained weights as input. The pre-trained weights for classification using kather100k data set is automatically downloaded if no weights are provided as input.
- Adds
- Adds mask-based patch extraction functionality to extract patches based on the regions that are highlighted in the
input_mask
. If'auto'
option is selected, a tissue mask is automatically generated for theinput_image
using tiatoolboxTissueMasker
functionality. - Adds visualisation module to overlay the results of an algorithm.
- Command line interface for stain normalisation can be called using the keyword
stain-norm
instead ofstainnorm
- Replaces
FixedWindowPatchExtractor
withSlidingWindowPatchExtractor
. - get_patchextractor takes the
slidingwindow
as an argument. - Depreciates
VariableWindowPatchExtractor
- Significantly improved python notebook documentation for clarity, consistency and ease of use for non-experts.
- Adds detailed installation instructions for Windows, Linux and Mac
- Moves flake8 above pytest in the
travis.yml
script stage. - Adds
set -e
at the start of the script stage intravis.yml
to cause it to exit on error and (hopefully) not run later parts of the stage. - Readthedocs related changes
- Uses
requirements.txt
in.readthedocs.yml
- Uses apt-get installation for openjpeg and openslide
- Removes conda build on readthedocs build
- Uses
- Adds extra checks to pre-commit, e.g., import sorting, spellcheck etc. Detailed list can be found on this commit.
- Add
TissueMasker
class to allow tissue masking usingOtsu
andMorphological
processing. - Add helper/convenience method to WSIReader(s) to produce a mask. Add reader object to allow reading a mask conveniently as if it were a WSI i.e., use same location and resolution to read tissue area and mask area.
- Add
PointsPatchExtractor
returns patches that can be used by classification models. Takescsv
,json
orpd.DataFrame
and returns patches corresponding to each pixel location. - Add feature
FixedWindowPatchExtractor
to run sliding window deep learning algorithms. - Add example notebooks for patch extraction and tissue masking.
- Update readme with improved instructions to use the toolbox. Make the README file somewhat more comprehensible to beginners, particularly those with not much background or experience.
tiatoolbox.dataloader
replaced bytiatoolbox.wsicore
- Minor bug fixes
- Improve unit test coverage.
- Move test data to tiatoolbox server.
- Fix URL for downloading test JP2 image.
- Update readme with new logo.
- Add
scikit-image
as dependency insetup.py
- Update notebooks to add instructions to install dependencies
- Adds
get_wsireader()
to return appropriate WSIReader. - Adds new functions to allow reading of regions using WSIReader at different resolutions given in units of:
- microns per-pixel (mpp)
- objective lens power (power)
- pixels-per baseline (baseline)
- resolution level (level)
- Adds functions for reading regions are
read_bounds
andread_rect
.read_bounds
takes a tuple (left, top, right, bottom) of coordinates in baseline (level 0) reference frame and returns a region bounded by those.read_rect
takes one coordinate in baseline reference frame and an output size in pixels.
- Adds
VirtualWSIReader
as a subclass of WSIReader which can be used to read visual fields (tiles).VirtualWSIReader
accepts ndarray or image path as input.
- Adds MPP fall back to standard TIFF resolution tags with warning.
- If OpenSlide cannot determine microns per pixel (
mpp
) from the metadata, checks the TIFF resolution units (TIFF tags:ResolutionUnit
,XResolution
andYResolution
) to calculate MPP. Additionally, add function to estimate missing objective power if MPP is known of derived from TIFF resolution tags.
- If OpenSlide cannot determine microns per pixel (
- Estimates missing objective power from MPP with warning.
- Adds example notebooks for stain normalisation and WSI reader.
- Adds caching to slide info property. This is done by checking if a private
self._m_info
exists and returning it if so, otherwiseself._info
is called to create the info for the first time (or to force regenerating) and the result is assigned toself._m_info
. This could in future be made much simpler with thefunctools.cached_property
decorator in Python 3.8+. - Adds pre processing step to stain normalisation where stain matrix encodes colour information from tissue region only.
read_region
refactored to be backwards compatible with openslide arguments.slide_info
changed toinfo
- Updates WSIReader which only takes one input
WSIReader
input_path
variable changed toinput_img
- Adds
tile_read_size
,tile_objective_value
andoutput_dir
to WSIReader.save_tiles() - Adds
tile_read_size
as a tuple transforms.imresize
takes additional argumentsoutput_size
and interpolation method 'optimise' which selectscv2.INTER_AREA
forscale_factor<1
andcv2.INTER_CUBIC
forscale_factor>1
- Refactors glymur code to use index slicing instead of deprecated read function.
- Refactors thumbnail code to use
read_bounds
and be a member of the WSIReader base class. - Updates
README.md
to clarify installation instructions. - Fixes slide_info.py for changes in WSIReader API.
- Fixes save_tiles.py for changes in WSIReader API.
- Updates
example_wsiread.ipynb
to reflect the changes in WSIReader. - Adds Google Colab and Kaggle links to allow user to run notebooks directly on colab or kaggle.
- Fixes a bug in taking directory input for stainnorm operation for command line interface.
- Pins
numpy<=1.19.3
to avoid compatibility issues with opencv. - Adds
scikit-image
orjupyterlab
as a dependency.
- Moved
test_wsireader_jp2_save_tiles
to test_wsireader.py. - Change recipe in Makefile for coverage to use pytest-cov instead of coverage.
- Runs travis only on PR.
- Adds pre-commit for easy setup of client-side git hooks for black code formatting and flake8 linting.
- Adds flake8-bugbear to pre-commit for catching potential deepsource errors.
- Adds constants for test regions in
test_wsireader.py
. - Rearranges
usage.rst
for better readability. - Adds
pre-commit
,flake8
,flake8-bugbear
,black
,pytest-cov
andrecommonmark
as dependency.
- Adds
OpenSlideWSIReader
to read Openslide image formats - Adds support to read Omnyx jp2 images using
OmnyxJP2WSIReader
. - New feature added to perform stain normalisation using
Ruifork
,Reinhard
,Vahadane
,Macenko
methods and using custom stain matrices. - Adds example notebook to read whole slide images via the toolbox.
- Adds
WSIMeta
class to save meta data for whole slide images.WSIMeta
casts properties to python types. Properties from OpenSlide are returned as string. raw values can always be accessed viaslide.raw
. Adds data validation e.g., checking that level_count matches up with the length of thelevel_dimensions
andlevel_downsamples
. Adds type hints toWSIMeta
. - Adds exceptions
FileNotSupported
andMethodNotSupported
- Restructures
WSIReader
as parent class to allow support to read whole slide images in other formats. - Adds
slide_info
as a property ofWSIReader
- Updates
slide_info
type toWSIMeta
fromdict
- Depreciates support for multiprocessing from within the toolbox. The toolbox is focused on processing single whole slide and standard images. External libraries can be used to run using multiprocessing on multiple files.
- Adds
scikit-learn
,glymur
as a dependency - Adds licence information
- Removes
pathos
as a dependency - Updates
openslide-python
requirement to 1.1.2
- Adds feature
read_region
to read a small region from whole slide images - Adds feature
save_tiles
to save image tiles from whole slide images - Adds feature
imresize
to resize images - Adds feature
transforms.background_composite
to avoid creation of black tiles from whole slide images.
- None
- Adds
pandas
as dependency
- None
- None
- Fix command line interface for
slide-info
feature and travis pypi deployment
- None
- None
- Minor changes to configuration files.
- Adds feature slide_info to read whole slide images and display meta data information
- Adds multiprocessing decorator TIAMultiProcess to allow running toolbox functions using multiprocessing.
- None
- Adds Sphinx Readthedocs support https://readthedocs.org/projects/tia-toolbox/ for stable and develop branches
- Adds code coverage tools to test the pytest coverage of the package
- Adds deepsource integration to highlight and fix bug risks, performance issues etc.
- Adds README to allow users to setup the environment.
- Adds conda and pip requirements instructions
- First release on PyPI.