Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

Tissue Image Analytics (TIA) Centre

Last update: Jan 8, 2023

Related tags

Deep Learning tiatoolbox

Overview

TIA Toolbox

Computational Pathology Toolbox developed at the TIA Centre

Getting Started

All Users

This package is for those interested in digital pathology: including graduate students, medical staff, members of the TIA Centre and of PathLAKE, and anyone, anywhere, who may find it useful. We will continue to improve this package, taking account of developments in pathology, microscopy, computing and related disciplines. Please send comments and criticisms to [email protected].

tiatoolbox is a multipurpose name that we use for 1) a certain computer program, 2) a Python package of related programs, created by us at the TIA Centre to help people get started in Digital Pathology, 3) this repository, 4) a certain virtual environment.

Developers

Anyone wanting to contribute to this repository, please first look at our Wiki and at our web page for contributors. See also the Prepare for development section of this document.

Links, if needed

The bash shell is available on all commonly encountered platforms. Commands in this README are in bash. Windows users can use the command prompt to install conda and python packages.

conda is a management system for software packages and virtual environments. To get conda, download Anaconda, which includes hundreds of the most useful Python packages, using 2GB disk space. Alternatively, miniconda uses 400MB, and packages can be added as needed.

Github is powered by the version control system git, which has many users and uses. In Github, it is used to track versions of code and other documents.

Examples Taster

Click here for jupyter notebooks, hosted on the web, with demos of tiatoolbox. All necessary resources to run the notebooks are remotely provided, so you don't need to have Python installed on your computer.
Click on a filename with suffix .ipynb and the notebook will open in your browser.
Click on one of the two blue checkboxes in your browser window labelled either Open in Colab or Open in Kaggle: colab and kaggle are websites providing free-of-charge platforms for running jupyter notebooks.
Operate the notebook in your browser, editing, inserting or deleting cells as desired.
Changes you make to the notebook will last no longer than your colab or kaggle session.

Install Python package

If you wish to use our programs, perhaps without developing them further, run the command pip install tiatoolbox or pip install --ignore-installed --upgrade tiatoolbox to upgrade from an existing installation. Detailed installation instructions can be found in the documentation.

To understand better how the programs work, study the jupyter notebooks referred to under the heading Examples Taster.

Command Line

tiatoolbox supports various features through command line. For more information, please try tiatoolbox --help

Prepare for development

Prepare a computer as a convenient platform for further development of the Python package tiatoolbox and related programs as follows.

Install pre-requisite software
Open a terminal window

    $ cd <future-home-of-tiatoolbox-directory>

Download a complete copy of the tiatoolbox.

    $ git clone https://github.com/TissueImageAnalytics/tiatoolbox.git

Change directory to tiatoolbox

    $ cd tiatoolbox

Create virtual environment for TIAToolbox using

    $ conda env create -f requirements.dev.conda.yml # for linux/mac only.
    $ conda activate tiatoolbox-dev

    $ conda create -n tiatoolbox-dev python=3.8 # select version of your choice
    $ conda activate tiatoolbox-dev
    $ pip install -r requirements_dev.txt

To use the packages installed in the environment, run the command:

    $ conda activate tiatoolbox-dev

License

The source code TIA Toolbox (tiatoolbox) as hosted on GitHub is released under the GNU General Public License (Version 3).

The full text of the licence is included in LICENSE.md.

Auxiliary Files

Auxiliary files, such as pre-trained model weights downloaded from the TIA Centre webpage (https://warwick.ac.uk/tia/), are provided under the Creative Commons Attribution-NonCommercial-ShareAlike Version 4 (CC BY-NC-SA 4.0) license.

Dual License

If you would like to use any of the source code or auxiliary files (e.g. pre-trained model weights) under a different license agreement please contact the Tissue Image Analytics (TIA) Centre at the University of Warwick ([email protected]).

Comments

DOC: update example_stainnorm.ipynb
Introductory markdown cell explaining why normalising stain can be a good idea. Copied several cells from example_wsiread.ipynb. It's a pity that there isn't (or I can't find) any mechanism for sharing cells that are identical in example_wsiread.ipynb and example_stainnorm.ipynb. This makes maintenance more difficult. One of the common cells is an initial cell in bash, banishing error messages and warning messages that previously occurred even when the user used the jupyter notebook correctly. I changed "normalize" to "normalise", and similarly for related words like "normalisation", which is the spelling adopted previously in the stainnorm module. I changed an erroneous "objective power x16" to the correct "objective power x20" in one markdown cell. In general, I made markdown cells somewhat shorter, more informative, clearer and used better English style. Having read a bit about Unit Tests, it doesn't seem to me that there are any appropriate tests, but I would like to see what the robot says about code coverage. Remaining tasks ( I need to research these) are:

set up pre-commit hooks

(try to) get Kaggle versions of the notebooks to work as expected.

documentation
opened by DavidBAEpstein 51
EG: Example notebook for nuclei instance segmentation

This PR is dedicated to HoVer-Net implementation in the TIAToolbox. I have included examples of using HoVer-Net on an image tile and a WSI. I tried to make it clear how a user can load and use the output of the prediction method. I have also shown the contour visualization functionality of the TIAToolbox.

opened by mostafajahanifar 42
[skip travis] EG: Add new example notebook for semantic segmentation functionality
A new example notebook is added based on the semantic segmentation functionality. In this notebook I tried to show two things: 1- How easy it is to to use pretrained segmentation models 2- How user can use his/her own segmentation model in the process.

I tried to explain all the concepts as simple as possible with a nice coherency.

TODO:

[x] Need to be checked on a local machine.

[x] Need to select a small breast cancer sample for WSI prediction part.

documentation
opened by mostafajahanifar 35
[skip ci] DOC: DFBR Jupyter Notebook

Notebook to show how to perform WSI registration using the DFBR method, followed by the BSpline approach.

Readthedocs link: https://tia-toolbox.readthedocs.io/en/dfbr_notebook/_notebooks/jnb/10-wsi_registration.html
documentation

opened by ruqayya 23
Add recipe for conda
TIA Toolbox version: N.A.

Python version: N.A.

Operating System: N.A.

Description

It would be great if this was available on conda in addition to pip.

What I Did

N.A.
dev tools
opened by sarthakpati 20
EXAMPLE: Add slide info notebook

Just as the title says. It consists of loading WSI and sample code for extracting patches using scipy or the provided functionality from tiatoolbox. And also a more descriptive explanation.

opened by vqdang 20
DEV: Add Pre-Commit Hook To Check Requirements Consistency
This adds a ~test~ pre-commit hook which checks that requirements files are consistent. It currently checks several criteria:

That for main/dev pairs (e.g. requirements.txt and requirements_dev.txt):

Any key present in main MUST also be in dev.

The version in main MUST match the version in dev.

The constraint in main MUST match the constraint dev.

For all requirements files (pip and conda), if the key is in AT LEAST TWO files:

The version MUST match across files with the requirement.

The constraint MUST match across files with the requirement.

It also prints out helpful errors such as:

AssertionError: shapely has different constraints: None (requirements.conda.yml) vs >= 1.8.1 (requirements.dev.conda.yml).

AssertionError: imagecodecs has inconsistent constraints: >=2021.11.20 (requirements.t xt), >=2021.11.20 (requirements_dev.txt), >2021.11.20 (requirements.conda.yml), >2021. 11.20 (requirements.dev.conda.yml).

I have also updated the constraints for the files based on these checks in this PR. I would like to turn this into a pre-commit hook (in addition to this or instead of?), but this is more of a proof of concept and feedback PR.

There is also a special case for setup.py. It is loaded differently but otherwise treated like pip requirements file.
question dev tools dependencies
opened by John-P 19
[skip travis] EG: add example notebook on advanced model techniques
Adding a new example notebook on semantic segmentation (more generally, model prediction) task where advanced topics are addressed. Particularly, in this notebook we are trying to demonestrate how user can use Tiatoolbox to solve the problems in the following three scenarios:

[x] 1. Instead of pretrained models embedded in Tiatoolbox's repsitory, user wants to use your own deep learning model (in Pytorch) in the Tiatoolbox prediction workflow.

[x] 2. User's input data is of an exotic form which the Tiatoolbox data stream functionality does not support by default.

documentation
opened by mostafajahanifar 19
ENH: Update the patch prediction example notebook based on new API

As the API has changed in the recently merged CNNPatchPredictor class, I have update the related example notebook to cover the changes and add some explanation about the new merge_predictions and overlay_patch_prediction functions.

opened by mostafajahanifar 17
DOC: Add examples of read_rect and read_bounds in wsi_reader notebook

Added examples for read_rect and read_bounds. There's bug we spotted in read_region method so didn't add it. The method is failing on every "level" argument.
documentation

opened by Srijay-lab 16
Image Resolution wrongly read in TIFFWSIReader
TIA Toolbox version: 1.2.1

Python version: 3.8

Operating System:

Description

According to the Tiff description, the tags XResolutionand YResolution are of type rational. Meaning that we should compute the fraction. In the code:

https://github.com/TissueImageAnalytics/tiatoolbox/blob/b3ace851ac61cbeee1f382b33925d8a9b0a1be55/tiatoolbox/wsicore/wsireader.py#L3245-L3248

we are only passing the first element. Instead we should pass res_x.value[0]/res_x.value[1] and res_y.value[0]/res_y.value[0]
bug
opened by rogertrullo 15
How to implement custom HoVer-Net Model in TIA ToolBox to segment nuclei?
TIA Toolbox version: 1.3.0

Python version: 3.9.12

Operating System: jupyter notebook on linux hpc cluster

Description

I used the 08-nucleus-instance-sgmentation.ipynb to segment nuclei. My dataset looks slightly different, than the pannuke dataset. So I used this tutorial to augment the pannuke dataset so that it looks like mine, and trained a new HoVer-Net model. The output i got is a .pt file.

Now I want to use my custom HoVer-Net model in the TIA ToolBox to segment nuclei.

How do I do that? Does anybody know, or has a link to an explanation/tutorial?
opened by WilliWespe 0
:construction_worker: DEV: Add Notebook URL Replacement Hook
This PR add a pre-commit hook which will replace URLs in notebook files to ensure they point to the current github rev (branch name). Patterns which are replaced are:

pip install tiatoolbox (e.g. pip install tiatoolbox -> pip install https://github.com/TissueImageAnalytics/tiatoolbox.git@dev-notebook-urls-hook)

dev tools
opened by John-P 2
:construction_worker: [pre-commit.ci] pre-commit autoupdate
updates:

github.com/pre-commit/pre-commit-hooks: v4.3.0 → v4.4.0

github.com/pycqa/isort: 5.10.1 → 5.11.4

github.com/psf/black: 22.10.0 → 22.12.0

github.com/PyCQA/flake8: 5.0.4 → 6.0.0
opened by pre-commit-ci[bot] 1

🚨 ENH: Expand Tile Server API

This draft PR adds to the tileserver a variety of endpoints required by the visualization interface for changing slide, changing overlays, and controlling various aspects of how rendererd annotations are displayed.

provided routes are:

"/tileserver/setup": setup user id upon a new connection
"/tileserver/change_color_prop/<prop>": change the property used to colour annotations by
"/tileserver/change_slide/<layer>/<layer_path>":  change the slide as specified in the path
"/tileserver/change_cmap/<cmap>": change colourmap
"/tileserver/load_annotations/<file_path>/<float:model_mpp>": load some annotations, adding to existing if present
"/tileserver/change_overlay/<overlay_path>": change or add an overlay. If provided a path to a file containing annotations, current annotation overlay layer is replaced. Image-based overlays are added as new layers.
"/tileserver/commit/<save_path>": save the current overlay database
"/tileserver/update_renderer/<prop>/<val>": generic updater for renderer props that don't need specific endpoint to handle them
"/tileserver/change_secondary_cmap/<type>/<prop>/<cmap>": specify a colormap override for one specific type
"/tileserver/get_props": get all unique properties that appear on annotations in the store
"/tileserver/reset": reset the server

opened by measty 3

Get ValueError: Unsupported axes `YX` when using OME-TIFF for nuclear segmentation

TIA Toolbox version: 1.3.0
Python version: 3.7
Operating System: Cent OS

Description

When I try to perform nuclear segmentation using an OME-TIFF file, I get the following error:

ValueError: Unsupported axes YX.

What I Did

wsi_output = objInstSegmentor.predict(
    ['TestSet_ROI_6291.ome.tiff'], 
    ioconfig=objIOConfig, 
    masks=None, 
    save_dir=f'WSITest/', 
    mode="wsi", 
    on_gpu=ON_GPU, 
    crash_on_exception=True, 
)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/local/55164614/ipykernel_10371/3581917802.py in <module>
     54     mode="wsi",
     55     on_gpu=ON_GPU,
---> 56     crash_on_exception=True,
     57 )

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/models/engine/semantic_segmentor.py in predict(self, imgs, masks, mode, on_gpu, ioconfig, patch_input_shape, patch_output_shape, stride_shape, resolution, units, save_dir, crash_on_exception)
   1326         for wsi_idx, img_path in enumerate(imgs):
   1327             self._predict_wsi_handle_exception(
-> 1328                 imgs, wsi_idx, img_path, mode, ioconfig, save_dir, crash_on_exception
   1329             )
   1330 

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/models/engine/semantic_segmentor.py in _predict_wsi_handle_exception(self, imgs, wsi_idx, img_path, mode, ioconfig, save_dir, crash_on_exception)
   1177             wsi_save_path = save_dir.joinpath(f"{wsi_idx}")
   1178             if crash_on_exception:
-> 1179                 raise err
   1180             logging.error("Crashed on %s", wsi_save_path)
   1181 

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/models/engine/semantic_segmentor.py in _predict_wsi_handle_exception(self, imgs, wsi_idx, img_path, mode, ioconfig, save_dir, crash_on_exception)
   1153         try:
   1154             wsi_save_path = save_dir.joinpath(f"{wsi_idx}")
-> 1155             self._predict_one_wsi(wsi_idx, ioconfig, str(wsi_save_path), mode)
   1156 
   1157             # Do not use dict with file name as key, because it can be

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/models/engine/nucleus_instance_segmentor.py in _predict_one_wsi(self, wsi_idx, ioconfig, save_path, mode)
    632         mask_path = None if self.masks is None else self.masks[wsi_idx]
    633         wsi_reader, mask_reader = self.get_reader(
--> 634             wsi_path, mask_path, mode, self.auto_generate_mask
    635         )
    636 

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/models/engine/semantic_segmentor.py in get_reader(img_path, mask_path, mode, auto_get_mask)
    670         """Define how to get reader for mask and source image."""
    671         img_path = pathlib.Path(img_path)
--> 672         reader = WSIReader.open(img_path)
    673 
    674         mask_reader = None

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/wsicore/wsireader.py in open(input_img, mpp, power)
    226 
    227         if suffixes[-2:] in ([".ome", ".tiff"],):
--> 228             return TIFFWSIReader(input_path, mpp=mpp, power=power)
    229 
    230         if last_suffix in (".tif", ".tiff") and is_tiled_tiff(input_path):

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/wsicore/wsireader.py in __init__(self, input_img, mpp, power, series, cache_size)
   3055                 return np.prod(self._canonical_shape(page.shape)[:2])
   3056 
-> 3057             series_areas = [page_area(s.pages[0]) for s in all_series]  # skipcq
   3058             self.series_n = np.argmax(series_areas)
   3059         self._tiff_series = self.tiff.series[self.series_n]

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/wsicore/wsireader.py in <listcomp>(.0)
   3055                 return np.prod(self._canonical_shape(page.shape)[:2])
   3056 
-> 3057             series_areas = [page_area(s.pages[0]) for s in all_series]  # skipcq
   3058             self.series_n = np.argmax(series_areas)
   3059         self._tiff_series = self.tiff.series[self.series_n]

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/wsicore/wsireader.py in page_area(page)
   3053             def page_area(page: tifffile.TiffPage) -> float:
   3054                 """Calculate the area of a page."""
-> 3055                 return np.prod(self._canonical_shape(page.shape)[:2])
   3056 
   3057             series_areas = [page_area(s.pages[0]) for s in all_series]  # skipcq

~/.conda/envs/single_cell/lib/python3.7/site-packages/tiatoolbox/wsicore/wsireader.py in _canonical_shape(self, shape)
   3088         if self._axes == "SYX":
   3089             return np.roll(shape, -1)
-> 3090         raise ValueError(f"Unsupported axes `{self._axes}`.")
   3091 
   3092     def _parse_svs_metadata(self) -> dict:

ValueError: Unsupported axes `YX`.

opened by Calebium 2

Releases(v1.3.1)

v1.3.1(Dec 20, 2022)
Major Updates and Feature Improvements

Adds NuClick architecture #449

Adds Annotation Store Reader #476

Adds DFBR method for registering pair of images #510

Changes to API

Adds a sample SVS loading function tiatoolbox.data.small_svs() to the data module #517

Bug Fixes and Other Changes

Simplifies example notebook for image reading for better readability

Restricts Shapely version to <2.0.0 for compatibility

Development related changes

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

Note: Please note that Python 3.7 support will be removed after this release. We plan to add support for Python 3.11 in the next release.
Source code(tar.gz)
Source code(zip)
v1.3.0(Oct 21, 2022)
Major Updates and Feature Improvements

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.

Changes to API

None

Bug Fixes and Other Changes

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.

Development related changes

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

Source code(tar.gz)
Source code(zip)
v1.2.1(Jul 7, 2022)
Major Updates and Feature Improvements

None

Changes to API

None

Bug Fixes and Other Changes

Fixes issues with dependencies

Adds flask to dependencies.

Fixes missing file in the python package

Clarifies help string for show-wsi option

Development related changes

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.

Source code(tar.gz)
Source code(zip)
v1.2.0(Jul 5, 2022)
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.

Source code(tar.gz)
Source code(zip)
v1.1.0(May 7, 2022)
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

Source code(tar.gz)
Source code(zip)
v1.0.1(Jan 31, 2022)
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

Source code(tar.gz)
Source code(zip)
v1.0.0(Dec 23, 2021)
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

Source code(tar.gz)
Source code(zip)
v0.8.0(Oct 28, 2021)
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

Source code(tar.gz)
Source code(zip)
v0.7.0(Sep 16, 2021)
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.

Source code(tar.gz)
Source code(zip)
v0.6.0(May 11, 2021)
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.

Source code(tar.gz)
Source code(zip)
v0.5.2(Mar 16, 2021)
Bug Fixes and Other Changes

Fix URL for downloading test JP2 image in test config (conftest.py) and notebooks.

Update readme with new logo.

Source code(tar.gz)
Source code(zip)
v0.5.1(Dec 31, 2020)
Bug Fixes and Other Changes

Adds scikit-image as dependency in setup.py

Updates notebooks to install dependencies

Source code(tar.gz)
Source code(zip)
v0.5.0(Dec 30, 2020)
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
Source code(tar.gz)
Source code(zip)
v0.4.0(Oct 25, 2020)
Major and Feature Improvements

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 tiatoolbox.

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 via slide.raw. Adds data validation e.g., checking that level_count matches up with the length of the level_dimensions and level_downsamples. Adds type hints to WSIMeta.

Adds exceptions FileNotSupported and MethodNotSupported

Changes to API

Restructures WSIReader as parent class to allow support to read whole slide images in other formats.

Adds slide_info as a property of WSIReader

Updates slide_info type to WSIMeta from dict

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 multi processing on multiple files.

Bug Fixes and Other Changes

Adds scikit-learn, glymur as a dependency

Adds licence information

Removes pathos as a dependency

Updates openslide-python requirement to 1.1.2

Co-authored-by: Shan E Ahmed @shaneahmed, John Pocock @John-P, Simon Graham @simongraham, Dang Vu @vqdang, Srijay Deshpande @Srijay-lab, Saad Bashir @rajasaad
Source code(tar.gz)
Source code(zip)
v0.3.0(Jul 18, 2020)
Major and Feature Improvements

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.

Changes to API

None

Bug Fixes and Other Changes

Adds pandas as dependency

Source code(tar.gz)
Source code(zip)
v0.2.2(Jul 12, 2020)
Major and Feature Improvements

None

Changes to API

None

Bug Fixes and Other Changes

Fix command line interface for slide-info feature and travis pypi deployment

Source code(tar.gz)
Source code(zip)
v0.2.1(Jul 10, 2020)
Major and Feature Improvements

None

Changes to API

None

Bug Fixes and Other Changes

Minor changes to configuration files.

Source code(tar.gz)
Source code(zip)
v0.2.0.0(Jul 10, 2020)
Major and Feature Improvements

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.

Changes to API

None

Bug Fixes and Other Changes

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

Updates setup.cfg to use double quotes for bump2version patterns.

Source code(tar.gz)
Source code(zip)
v0.1.1(Jun 19, 2020)

Initial test release.
Source code(tar.gz)
Source code(zip)

Owner

Tissue Image Analytics (TIA) Centre

From tissue images to insights

GitHub

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Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

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All Users

Developers

Links, if needed

Examples Taster

Install Python package

Command Line

Prepare for development

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Releases(v1.3.1)

v1.3.1(Dec 20, 2022)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.3.0(Oct 21, 2022)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.2.1(Jul 7, 2022)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.2.0(Jul 5, 2022)

1.2.0 (2022-07-05)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.1.0(May 7, 2022)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.0.1(Jan 31, 2022)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v1.0.0(Dec 23, 2021)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v0.8.0(Oct 28, 2021)

Major Updates and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v0.7.0(Sep 16, 2021)

Major and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development related changes

v0.6.0(May 11, 2021)

Major and Feature Improvements

Changes to API

Bug Fixes and Other Changes

Development-related changes

v0.5.2(Mar 16, 2021)

Bug Fixes and Other Changes

v0.5.1(Dec 31, 2020)

Bug Fixes and Other Changes

v0.5.0(Dec 30, 2020)

Major and Feature Improvements