:zap: Improve Metadata Handling for WSI Readers by shaneahmed · Pull Request #1001 · TissueImageAnalytics/tiatoolbox

shaneahmed · 2026-02-20T12:34:41Z

Summary

This PR standardises and improves metadata inference across all WSI readers by introducing a unified mechanism for estimating missing objective power and MPP. It updates all major reader implementations (TIFF, DICOM, OpenSlide, JP2, NGFF, fsspec), fixes reader‑selection ordering, and adds extensive tests to validate inference behaviour and warnings. New sample data is included to support expanded DICOM metadata coverage.

🔑 Key Changes

1. Centralised Metadata Inference

Introduces WSIReader._estimate_mpp_objective_power() as the shared method for inferring missing objective power and MPP.
Removes duplicated inference logic and ensures consistent fallback behaviour across all readers.

2. Unified Metadata Handling Across Readers

All major WSI readers now use the central inference method:

TIFFWSIReader
DICOMWSIReader
OpenSlideWSIReader
JP2WSIReader
NGFFWSIReader
FsspecJsonWSIReader

This ensures consistent behaviour when metadata is missing or partially defined.

3. Improved Reader Selection Logic

Adds try_openslide() and updates selection priority so TIFF files are first attempted via OpenSlide.
Fixes misclassification issues where TIFF inputs were incorrectly routed to other readers.

4. Expanded and Strengthened Test Coverage

New and updated tests now cover:

Missing or partial OME‑TIFF metadata
Missing MPP (X/Y)
Missing instrument references
Warning behaviour when inference is required
DICOM metadata with and without optical path information
New dicom-2 sample with known objective/MPP values

Assertions have been updated to reflect the new inference logic.

5. Updated Remote Sample Data

Replaces CMU-1.dicom.zip with CMU-1-Small-Region.dicom.zip.
Adds new dicom-2 sample (JP2K-33003-1.zip) to support metadata‑specific tests.

6. Cleanup and Minor Fixes

Corrects import path for TransformedWSIReader.
Improves type hints in objective_power2mpp.
Normalises ndarray conversion for inferred MPP values.
Cleans up mypy issues related to dimension and metadata handling.

This PR resolves Jupyter Notebook 10 – WSI Reading (#998) and KongNet Notebook for MONKEY dataset (#987).

- Try OpenSlide Reader for tiff files first - Fallback to calculating objective power from mpp

codecov · 2026-02-20T12:54:20Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.53%. Comparing base (e401b71) to head (2521f5f).
⚠️ Report is 1 commits behind head on develop.

Additional details and impacted files

@@           Coverage Diff            @@
##           develop    #1001   +/-   ##
========================================
  Coverage    99.53%   99.53%           
========================================
  Files           83       83           
  Lines        11353    11397   +44     
  Branches      1493     1499    +6     
========================================
+ Hits         11300    11344   +44     
  Misses          28       28           
  Partials        25       25

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Copilot

Pull request overview

This PR standardizes metadata inference (objective power and MPP) across WSI readers by introducing a shared inference helper and updating multiple reader implementations and tests. It also adjusts reader-selection priority so TIFF inputs are attempted via OpenSlide first.

Changes:

Add a centralized WSIReader._estimate_mpp_objective_power() and use it across multiple readers.
Update reader selection to try OpenSlide first for .tif/.tiff.
Update remote samples and expand tests for DICOM/TIFF metadata inference and warning behavior.

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 7 comments.

Show a summary per file

File	Description
`tiatoolbox/wsicore/wsireader.py`	Adds OpenSlide-first selection for TIFF and introduces centralized MPP/objective-power inference used across readers.
`tiatoolbox/utils/misc.py`	Broadens typing for `objective_power2mpp` to accept `np.ndarray`.
`tiatoolbox/data/remote_samples.yaml`	Updates DICOM sample filename and adds a second DICOM sample entry for new metadata tests.
`tests/test_wsireader.py`	Adds/updates DICOM metadata assertions and adds a new test for objective-power presence/inference.
`tests/test_tiffreader.py`	Updates OME-TIFF metadata tests and adds a warning-logging test for missing metadata.

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measty

This introduces a regression on that ome.tiff you sent me a while ago to check multi-channel reader on (20240625_144804_1_08TcnB_Kidney_panel_June_RP_52top51bottom.ome.tiff).

In develop, pyramid is seen:

Whereas opening the same slide in this PR:

No pyramid is seen so it is slow and uses loads of memory, and the slide doesn't display right (seems to be black & white?)

shaneahmed · 2026-03-06T04:44:36Z

This introduces a regression on that ome.tiff you sent me a while ago to check multi-channel reader on (20240625_144804_1_08TcnB_Kidney_panel_June_RP_52top51bottom.ome.tiff).

In develop, pyramid is seen:
Whereas opening the same slide in this PR: No pyramid is seen so it is slow and uses loads of memory, and the slide doesn't display right (seems to be black & white?)

Is this because of openslide reader? Probably, we can now remove openslidereader first as now meta data is handled better.

shaneahmed · 2026-03-06T10:02:41Z

This introduces a regression on that ome.tiff you sent me a while ago to check multi-channel reader on (20240625_144804_1_08TcnB_Kidney_panel_June_RP_52top51bottom.ome.tiff).

In develop, pyramid is seen:
Whereas opening the same slide in this PR: No pyramid is seen so it is slow and uses loads of memory, and the slide doesn't display right (seems to be black & white?)

@measty This commit 4c0ba9a resolves this issue. I have tested the WSI Registration notebook and the mIF images, both work fine now. However, it fails on MONKEY challenge image.

- Update wsireader mpp checks

@aacic

## TIAToolbox v2.0.0 (2026-03-11) ### ✨ Major Updates and Feature Improvements #### ⚙️ Engine Redesign (PR #578) TIAToolbox 2.0.0 introduces a completely re-engineered inference engine designed for significant performance, scalability, and memory-efficiency improvements. #### Key Enhancements - A modern processing stack built on **Dask** (parallel/distributed execution) and **Zarr** (chunked, out-of-core storage) - **Standardised output formats** across all engines: - Python `dict` - **Zarr** - **AnnotationStore** (SQLite-backed) - **QuPath JSON** - Cleaner runtime behavior with reduced warning noise and a unified progress bar - More predictable memory usage through chunked streaming - Broader test coverage across engine components ### 🗺️ Improved QuPath Support Enhancements include: - Better handling of **GeoJSON** - Support for **multipoint geometries** (#841) - Improved semantic output helpers: - `dict_to_store_semantic_segmentor` (#926) - OME-TIFF probability overlays (#929) ### 🔬 New Nucleus Detection Engine A dedicated nucleus detection pipeline has been added, built on the redesigned engine for improved accuracy and efficient large-scale processing. #### 🧠 KongNet Model Family TIAToolbox 2.0.0 introduces **KongNet**, a high-performance architecture that achieved top results across multiple international challenges: - 🥇 **1st place: MONKEY Challenge (overall detection)** - 🥇 **1st place: MIDOG (mitosis detection)** - ⭐ Top-tier performance on **PUMA** Multiple pretrained variants are available (CoNIC, PanNuke, MONKEY, PUMA, MIDOG), each with standardised IO configurations. ### 🧬 Expanded Foundation Model Support Additional foundation models are now supported (#906), broadening the range of high-capacity architectures available for feature extraction and downstream tasks. ### 🖼️ SAM Segmentation in TIAViz TIAViz now integrates Meta’s Segment Anything Model (SAM), enabling: - Interactive segmentation - Rapid region extraction - Exploratory annotation workflows Simplified SAM usage (#968) streamlines its integration into analysis pipelines. ### 🧩 Enhanced WSIReader & Metadata Handling Major improvements include: - More robust cross-vendor **metadata extraction** (#1001) - **Multichannel image support** (PR #825) for immunofluorescence and non-RGB modalities - Simplified Windows installation using `openslide-bin` (no manual DLL steps) - macOS Tileserver fix (#976) - Improved DICOM reading (#934) ### ☁️ New Cloud-Native Reader: FsspecJSONWSIReader (PR #897) A new reader supporting **fsspec-compatible filesystems**, enabling seamless access to WSIs stored on: - S3 - GCS - Azure - HPC clusters - Any fsspec-supported backend This enables cloud-native and distributed data workflows. Contributed by @aacic ### 🤗 Pretrained Models Migrated to Hugging Face All pretrained models and sample assets have been migrated (#945, #983), improving: - Download reliability - Versioning and reproducibility - Caching and CI integration - Licensing clarity per model family ### 🛡️ Security, Compatibility & Tooling #### 🔐 Security & Dependency Updates - Dependency upgrades - Internal security improvements - Explicit workflow permissions added (#1021, #1023) #### 🐍 Python Version Support - **Dropped:** Python **3.9** - **Added:** Python **3.13** - **Supported:** Python 3.10–3.13 - Updated CUDA wheel source to **cu126** #### 🛠️ Developer Tooling & CI/CD - Expanded **mypy** type-checking coverage (#912, #931, #935, #951) - Updated pre-commit hooks and general formatting - CI uses **CPU-only PyTorch** for faster, more reliable builds (#974, #979) - Updated pip install workflow (#1013) - Added new **Python 3.13 Docker images** (#1014, #1019) ### 🧹 Bug Fixes & Stability Improvements - Fixed multi-GPU behaviour with `torch.compile` (#923) - Fixed DICOM reading issue (#934) - Fixed annotation contour handling with holes (#956) - Fixed consecutive annotation load bug (#927) - Fixed SCCNN model issues (#970) - Fixed MapDe `dist_filter` shape issue (#914) - Improved notebook reliability on Colab (#1026–#1030) - macOS TileServer issues resolved (#976) ### 🧭 Migration Guide for Users #### 🔄 Updating from 1.x to 2.0.0 #### Update calls: replace `.predict()` with `.run()` ```python # Old results = segmentor.predict(imgs=[...], ioconfig=config) # New results = segmentor.run(images=[...], ioconfig=config) ``` #### Use `patch_mode`: replace `mode="patch"` with `patch_mode=True` and `mode="tile"` or "wsi" with `patch_mode=False` ```python # Old results = segmentor.predict(imgs=[...], mode="patch", ioconfig=config) # New results = segmentor.run(images=[...], patch_mode=True, ioconfig=config) ``` ```python # Old results = segmentor.predict(imgs=[...], mode="wsi", ioconfig=config) # New results = segmentor.run(images=[...], patch_mode=False, ioconfig=config) ``` #### Use the new I/O configs ```python from tiatoolbox.models.engine.io_config import IOSegmentorConfig config = IOSegmentorConfig( patch_input_shape=(256, 256), stride_shape=(240, 240), input_resolutions=[{"resolution": 0.25, "units": "mpp"}], save_resolution={"units": "baseline", "resolution": 1.0} ) ``` #### Specify the output format ```python results = segmentor.run( images=[...], ioconfig=ioconfig, output_type="zarr", # or "dict", "annotationstore", "qupath" save_dir="outputs/" ) ``` #### Update imports - `tiatoolbox.typing` → `tiatoolbox.type_hints` #### Install requirements - Python **3.10+** required - On Windows: install OpenSlide via `pip install openslide-bin` **Full Changelog:** v1.6.0...v2.0.0 --------- Signed-off-by: Shan E Ahmed Raza <13048456+shaneahmed@users.noreply.github.com> Co-authored-by: measty <20169086+measty@users.noreply.github.com> Co-authored-by: Jiaqi-Lv <60471431+Jiaqi-Lv@users.noreply.github.com> Co-authored-by: adamshephard <39619155+adamshephard@users.noreply.github.com> Co-authored-by: Mostafa Jahanifar <74412979+mostafajahanifar@users.noreply.github.com> Co-authored-by: John Pocock <John-P@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yijie Zhu <120978607+YijieZhu15@users.noreply.github.com> Co-authored-by: Aleksandar Acic <32873451+aacic@users.noreply.github.com> Co-authored-by: Abdol A <u2271662@live.warwick.ac.uk> Co-authored-by: Abishek <abishekraj6797@gmail.com> Co-authored-by: behnazelhaminia <30952176+behnazelhaminia@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Adam Shephard <adam.shephard@warwick.ac.uk> Co-authored-by: gozdeg <gozdegunesli@gmail.com> Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Co-authored-by: mbasheer04 <78800844+mbasheer04@users.noreply.github.com> Co-authored-by: vqdang <24943262+vqdang@users.noreply.github.com>

@aacic

🔖 Release 2.0.0 (#1031) ## TIAToolbox v2.0.0 (2026-03-11) ### ✨ Major Updates and Feature Improvements #### ⚙️ Engine Redesign (PR #578) TIAToolbox 2.0.0 introduces a completely re-engineered inference engine designed for significant performance, scalability, and memory-efficiency improvements. #### Key Enhancements - A modern processing stack built on **Dask** (parallel/distributed execution) and **Zarr** (chunked, out-of-core storage) - **Standardised output formats** across all engines: - Python `dict` - **Zarr** - **AnnotationStore** (SQLite-backed) - **QuPath JSON** - Cleaner runtime behavior with reduced warning noise and a unified progress bar - More predictable memory usage through chunked streaming - Broader test coverage across engine components ### 🗺️ Improved QuPath Support Enhancements include: - Better handling of **GeoJSON** - Support for **multipoint geometries** (#841) - Improved semantic output helpers: - `dict_to_store_semantic_segmentor` (#926) - OME-TIFF probability overlays (#929) ### 🔬 New Nucleus Detection Engine A dedicated nucleus detection pipeline has been added, built on the redesigned engine for improved accuracy and efficient large-scale processing. #### 🧠 KongNet Model Family TIAToolbox 2.0.0 introduces **KongNet**, a high-performance architecture that achieved top results across multiple international challenges: - 🥇 **1st place: MONKEY Challenge (overall detection)** - 🥇 **1st place: MIDOG (mitosis detection)** - ⭐ Top-tier performance on **PUMA** Multiple pretrained variants are available (CoNIC, PanNuke, MONKEY, PUMA, MIDOG), each with standardised IO configurations. ### 🧬 Expanded Foundation Model Support Additional foundation models are now supported (#906), broadening the range of high-capacity architectures available for feature extraction and downstream tasks. ### 🖼️ SAM Segmentation in TIAViz TIAViz now integrates Meta’s Segment Anything Model (SAM), enabling: - Interactive segmentation - Rapid region extraction - Exploratory annotation workflows Simplified SAM usage (#968) streamlines its integration into analysis pipelines. ### 🧩 Enhanced WSIReader & Metadata Handling Major improvements include: - More robust cross-vendor **metadata extraction** (#1001) - **Multichannel image support** (PR #825) for immunofluorescence and non-RGB modalities - Simplified Windows installation using `openslide-bin` (no manual DLL steps) - macOS Tileserver fix (#976) - Improved DICOM reading (#934) ### ☁️ New Cloud-Native Reader: FsspecJSONWSIReader (PR #897) A new reader supporting **fsspec-compatible filesystems**, enabling seamless access to WSIs stored on: - S3 - GCS - Azure - HPC clusters - Any fsspec-supported backend This enables cloud-native and distributed data workflows. Contributed by @aacic ### 🤗 Pretrained Models Migrated to Hugging Face All pretrained models and sample assets have been migrated (#945, #983), improving: - Download reliability - Versioning and reproducibility - Caching and CI integration - Licensing clarity per model family ### 🛡️ Security, Compatibility & Tooling #### 🔐 Security & Dependency Updates - Dependency upgrades - Internal security improvements - Explicit workflow permissions added (#1021, #1023) #### 🐍 Python Version Support - **Dropped:** Python **3.9** - **Added:** Python **3.13** - **Supported:** Python 3.10–3.13 - Updated CUDA wheel source to **cu126** #### 🛠️ Developer Tooling & CI/CD - Expanded **mypy** type-checking coverage (#912, #931, #935, #951) - Updated pre-commit hooks and general formatting - CI uses **CPU-only PyTorch** for faster, more reliable builds (#974, #979) - Updated pip install workflow (#1013) - Added new **Python 3.13 Docker images** (#1014, #1019) ### 🧹 Bug Fixes & Stability Improvements - Fixed multi-GPU behaviour with `torch.compile` (#923) - Fixed DICOM reading issue (#934) - Fixed annotation contour handling with holes (#956) - Fixed consecutive annotation load bug (#927) - Fixed SCCNN model issues (#970) - Fixed MapDe `dist_filter` shape issue (#914) - Improved notebook reliability on Colab (#1026–#1030) - macOS TileServer issues resolved (#976) ### 🧭 Migration Guide for Users #### 🔄 Updating from 1.x to 2.0.0 #### Update calls: replace `.predict()` with `.run()` ```python # Old results = segmentor.predict(imgs=[...], ioconfig=config) # New results = segmentor.run(images=[...], ioconfig=config) ``` #### Use `patch_mode`: replace `mode="patch"` with `patch_mode=True` and `mode="tile"` or "wsi" with `patch_mode=False` ```python # Old results = segmentor.predict(imgs=[...], mode="patch", ioconfig=config) # New results = segmentor.run(images=[...], patch_mode=True, ioconfig=config) ``` ```python # Old results = segmentor.predict(imgs=[...], mode="wsi", ioconfig=config) # New results = segmentor.run(images=[...], patch_mode=False, ioconfig=config) ``` #### Use the new I/O configs ```python from tiatoolbox.models.engine.io_config import IOSegmentorConfig config = IOSegmentorConfig( patch_input_shape=(256, 256), stride_shape=(240, 240), input_resolutions=[{"resolution": 0.25, "units": "mpp"}], save_resolution={"units": "baseline", "resolution": 1.0} ) ``` #### Specify the output format ```python results = segmentor.run( images=[...], ioconfig=ioconfig, output_type="zarr", # or "dict", "annotationstore", "qupath" save_dir="outputs/" ) ``` #### Update imports - `tiatoolbox.typing` → `tiatoolbox.type_hints` #### Install requirements - Python **3.10+** required - On Windows: install OpenSlide via `pip install openslide-bin` **Full Changelog:** v1.6.0...v2.0.0 --------- Signed-off-by: Shan E Ahmed Raza <13048456+shaneahmed@users.noreply.github.com> Co-authored-by: measty <20169086+measty@users.noreply.github.com> Co-authored-by: Jiaqi-Lv <60471431+Jiaqi-Lv@users.noreply.github.com> Co-authored-by: adamshephard <39619155+adamshephard@users.noreply.github.com> Co-authored-by: Mostafa Jahanifar <74412979+mostafajahanifar@users.noreply.github.com> Co-authored-by: John Pocock <John-P@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yijie Zhu <120978607+YijieZhu15@users.noreply.github.com> Co-authored-by: Aleksandar Acic <32873451+aacic@users.noreply.github.com> Co-authored-by: Abdol A <u2271662@live.warwick.ac.uk> Co-authored-by: Abishek <abishekraj6797@gmail.com> Co-authored-by: behnazelhaminia <30952176+behnazelhaminia@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Adam Shephard <adam.shephard@warwick.ac.uk> Co-authored-by: gozdeg <gozdegunesli@gmail.com> Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Co-authored-by: mbasheer04 <78800844+mbasheer04@users.noreply.github.com> Co-authored-by: vqdang <24943262+vqdang@users.noreply.github.com>

🐛 Try OpenSlide Reader for tiff files first

fc8b82d

- Try OpenSlide Reader for tiff files first - Fallback to calculating objective power from mpp

shaneahmed self-assigned this Feb 20, 2026

shaneahmed added this to the Release v2.0.0 milestone Feb 20, 2026

shaneahmed added bug Something isn't working dev tools Changes/Updates in Development tools labels Feb 20, 2026

shaneahmed requested a review from measty February 20, 2026 12:36

✅ Update tests for coverage

8e9d67e

This was referenced Feb 20, 2026

📝 Update Jupyter Notebooks 10-wsi-registration.ipynb #998

Merged

🆕 Add KongNet Model and Example Notebook #987

Merged

shaneahmed added 3 commits March 3, 2026 13:24

Merge branch 'develop' into bug-fix-openslide-read

b504b7d

🚀 Add estimation of mpp and objective power for all the readers

649d421

✅ Add DICOM objective power estimation

5d7f9d6

shaneahmed changed the title ~~🐛 Try OpenSlideWSIReader for tiff Files First~~ ⚡ Improve Metadata Handling for WSI Readers Mar 5, 2026

Merge branch 'develop' into bug-fix-openslide-read

be1ab87

Jiaqi-Lv requested a review from Copilot March 5, 2026 13:25

Copilot started reviewing on behalf of Jiaqi-Lv March 5, 2026 13:26 View session

Copilot AI reviewed Mar 5, 2026

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💡 Address Co-Pilot comments

0877dfb

measty reviewed Mar 6, 2026

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Comment thread tiatoolbox/wsicore/wsireader.py Outdated

🔥 Remove openslide_reader read

4c0ba9a

shaneahmed and others added 7 commits March 6, 2026 10:29

👍 Add OpenSlide as a backup

3c11f7e

Merge branch 'develop' into bug-fix-openslide-read

e70ac54

✅ Add test for try_openslide.

7b7f728

update WSIPatchDataset to support multithreading for Linux

20f6346

Merge branch 'develop' into bug-fix-openslide-read

2e1423b

🐛 Update __get__item__ to pass wsireader_kwargs

3697cf6

- Update wsireader mpp checks

🐛 Update get_wsi_output_shape to pass wsireader_kwargs

4e04fd1

shaneahmed added 3 commits March 9, 2026 15:13

Merge branch 'develop' into bug-fix-openslide-read

1a0e1a1

💬 Address AI comments

454a782

Merge branch 'develop' into bug-fix-openslide-read

2521f5f

shaneahmed merged commit 22e26d8 into develop Mar 10, 2026
19 checks passed

shaneahmed deleted the bug-fix-openslide-read branch March 10, 2026 11:27

shaneahmed mentioned this pull request Mar 11, 2026

🔖 Release 2.0.0 #1031

Merged

shaneahmed mentioned this pull request Mar 11, 2026

🔖 Release 2.0.0 #1033

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⚡ Improve Metadata Handling for WSI Readers#1001

⚡ Improve Metadata Handling for WSI Readers#1001
shaneahmed merged 18 commits intodevelopfrom
bug-fix-openslide-read

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Summary

🔑 Key Changes

1. Centralised Metadata Inference

2. Unified Metadata Handling Across Readers

3. Improved Reader Selection Logic

4. Expanded and Strengthened Test Coverage

5. Updated Remote Sample Data

6. Cleanup and Minor Fixes

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