TIAToolbox 1.2.0

1.2.0 (2022-07-05)

Major Updates and Feature Improvements

• 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.

Changes to API

• Renames stainnorm_target() function to stain_norm_target().
• Removes get_wsireader
• Replaces the custom PlattScaler in tools/scale.py with the regular Scikit-Learn LogisticRegression.

Bug Fixes and Other Changes

• 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() and return_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.

Development related changes

• Adds pre-commit hooks for consistency across the repo.
• Sets up GitHub Actions Workflow.
  • Travis CI will be removed in future release.

TIAToolbox 1.1.0

• Updates AUTHORS.md

Major Updates and Feature Improvements

• 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: @ByteHexler.
• Adds benchmark for Annotations Store.

Changes to API

• None

Bug Fixes and Other Changes

• 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.

Development related changes

• Adds pydicom and wsidicom 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

TIAToolbox 1.0.1

Major Updates and Feature Improvements

• Updates dependencies for conda recipe #262
  • External Contribution : @sarthakpati

Changes to API

• None

Bug Fixes and Other Changes

• Adds User Warning For Missing SQLite Functions
• Fixes Pixman version check errors
• Fixes empty query in instance segmentor

Development related changes

• Fixes flake8 linting issues and typos
• Conditional pytest.skipif to skip GPU tests on travis while running them locally or elsewhere

TIAToolbox 1.0.0

Major Updates and Feature Improvements

• 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

Changes to API

• Replaces models.controller API with models.engine
• Replaces CNNPatchPredictor with PatchPredictor

Bug Fixes and Other Changes

• 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 in pretrained_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.
• Improves performance of mask based patch extraction

Development related changes

• 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

TIAToolbox 0.8.0

Major Updates and Feature Improvements

• 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 to pretrained_weights.
• Adds cli for semantic segmentation.
• Update python notebooks to add read_rect and read_bounds examples with mpp read.

Changes to API

• Adds WSIReader.open. get_wsireader will deprecate in the next release. Please use WSIReader.open instead.
• CLI is now POSIX compatible
  • Replaces underscores in variable names with hyphens
• Models API updated to use pretrained_weights instead of pretrained_weight.
• Move string_to_tuple to tiatoolbox/utils/misc.py

Bug Fixes and Other Changes

• 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

Development related changes

• Adds dependencies for tiffile, imagecodecs, zarr.
• Adds more stringent pre-commit checks
• Moved local test files into tiatoolbox/data.
• Fixed Manifest.ini and added tiatoolbox/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) in tiatoolbox.data.
• Removed duplicate code in conftest.py for downloading remote files. This is now in tiatoolbox.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

TIAToolbox 0.7.0

Major and Feature Improvements

• 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 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 the input_image using tiatoolbox TissueMasker functionality.
• Adds visualisation module to overlay the results of an algorithm.

Changes to API

• Command line interface for stain normalisation can be called using the keyword stain-norm instead of stainnorm
• Replaces FixedWindowPatchExtractor with SlidingWindowPatchExtractor .
• get_patchextractor takes the slidingwindow as an argument.
• Depreciates VariableWindowPatchExtractor

Bug Fixes and Other Changes

• Significantly improved python notebook documentation for clarity, consistency and ease of use for non-experts.
• Adds detailed installation instructions for Windows, Linux and Mac

Development related changes

• Moves flake8 above pytest in the travis.yml script stage.
• Adds set -e at the start of the script stage in travis.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
• Adds extra checks to pre-commit, e.g., import sorting, spellcheck etc. Detailed list can be found on this commit.

TIAToolbox 0.6.0

Major and Feature Improvements

• Add TissueMasker class to allow tissue masking using Otsu and Morphological 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. Takes csv, json or pd.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.

Changes to API

• tiatoolbox.dataloader replaced by tiatoolbox.wsicore

Bug Fixes and Other Changes

• Minor bug fixes

Development-related changes

• Improve unit test coverage.
• Move test data to tiatoolbox server.

TIAToolbox 0.5.2

Bug Fixes and Other Changes

• Fix URL for downloading test JP2 image in test config (conftest.py) and notebooks.
• Update readme with new logo.

TIAToolbox 0.5.1

Bug Fixes and Other Changes

• Adds scikit-image as dependency in setup.py
• Updates notebooks to install dependencies

TIAToolbox 0.5.0

Major and Feature Improvements

• 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 and read_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 and YResolution) to calculate MPP. Additionally, add function to estimate missing objective power if MPP is known of derived from TIFF resolution tags.
• 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, otherwise self._info is called to create the info for the first time (or to force regenerating) and the result is assigned to self._m_info. This could in future be made much simpler with the functools.cached_property decorator in Python 3.8+.
• Adds pre processing step to stain normalisation where stain matrix encodes colour information from tissue region only.

Changes to API

• read_region refactored to be backwards compatible with openslide arguments.
• slide_info changed to info
• Updates WSIReader which only takes one input
• WSIReader input_path variable changed to input_img
• Adds tile_read_size, tile_objective_value and output_dir to WSIReader.save_tiles()
• Adds tile_read_size as a tuple
• transforms.imresize takes additional arguments output_size and interpolation method 'optimise' which selects cv2.INTER_AREA for scale_factor<1 and cv2.INTER_CUBIC for scale_factor>1

Bug Fixes and Other Changes

• 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 or jupyterlab as a dependency.

Development related changes

• 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 and recommonmark as dependency.

Co-authored-by: Shan E Ahmed Raza @shaneahmed, John Pocock @John-P, Simon Graham @simongraham, Dang Vu @vqdang, Mostafa Jahanifar @mostafajahanifar Srijay Deshpande @Srijay-lab, Saad Bashir @rajasaad