{"name":"pentachrome-plugin","display_name":"Pentachrome Pipeline","visibility":"public","icon":null,"categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"pentachrome-plugin.guided","title":"Guided Analysis","python_name":"pentachrome_plugin._guided_widget:GuidedWidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"pentachrome-plugin.guided","display_name":"Guided Analysis","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"pentachrome-plugin","version":"0.4.6","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"Napari plugin for the Pentachrome histology pipeline: VSI extraction, nnUNet inference, statistics.","description":"# pentachrome-plugin\n\nNapari plugin for the Pentachrome histology pipeline. The public plugin exposes a\nsingle clinician-facing widget, **Guided Analysis**, that walks through the whole\nworkflow in one pane:\n\n1. **Extract** — pull tissue-region TIFFs out of Olympus `.vsi` files.\n2. **Detect** — run the trained nnUNet Epithelium / MultiStructure models on those\n   images and load colorized masks back into the viewer.\n3. **Measure** — per-region statistics (thickness, composition, cell densities),\n   with CSV export.\n\nEach phase also exists as a separate advanced widget (`VsiExtractorWidget`,\n`NnUnetInferenceWidget`, `AnalysisWidget`) used during development. These are **not**\nregistered in the public menu — see [From source (development)](#from-source-development).\n\nSource and issues: https://github.com/dtsilis7/Pentrachrome-Pipeline\n\n**Requirements:** Windows, Python 3.10, napari >= 0.4.18, a JDK 17, the\nJava/bioformats stack, and the nnUNet model weights. The Java stack and the weights\nare installed separately (see below) — they can't come from a plain `pip install`.\n\n## Installation (Windows, PowerShell)\n\nA working install has **three parts**, in this order:\n\n1. A conda environment (Python 3.10) with NumPy<2, a JDK, and the Java/bioformats stack\n2. The plugin itself\n3. The nnUNet model weights (downloaded separately)\n\n> ⚠️ **`pip install pentachrome-plugin` on its own is not enough.** It gives you\n> the napari UI, but **VSI extraction fails** without the Java/bioformats stack and\n> **inference fails** without the model weights. Installing through napari's plugin\n> manager only does the pip part — you still need steps 1 and 3 in the *same* env.\n\n### Step 1 — environment + Java/bioformats stack\n\n`openjdk` and `numpy<2` come from conda-forge, but **`python-javabridge` and\n`python-bioformats` are not packaged on conda-forge — they come from pip.**\nKeeping Python at 3.10 lets pip grab the prebuilt `python-javabridge` wheel instead\nof compiling it.\n\n```powershell\nconda create -n pentachrome python=3.10 -y\nconda activate pentachrome\nconda install -c conda-forge openjdk=17 \"numpy<2\" -y    # JDK + NumPy 1.x FIRST\npip install python-javabridge python-bioformats          # Java wrappers (pip, not conda)\n```\n\nOrder matters: `numpy<2` must be in place **before** javabridge installs, because\nthe javabridge C extension is built against the NumPy 1.x ABI. If pip can't find a\nprebuilt wheel for your Python and falls back to compiling, add `--no-build-isolation`\nto the pip line (so the build sees the pinned NumPy) and make sure the MS C++ Build\nTools and the JDK are present.\n\n### Step 2 — the plugin\n\n```powershell\npip install pentachrome-plugin\npip install nnunetv2          # required for the Detect step\n```\n\nYou can also install the plugin through napari's **Plugins -> Install/Uninstall\nPlugins** dialog (search \"pentachrome\"), but that only covers this step — you still\nneed Step 1 and Step 3 in the same environment.\n\n### Step 3 — model weights\n\nDownload the nnUNet weights and point the widget at them — see\n[Model weights](#model-weights) below.\n\n### Verify\n\n```powershell\npython -m napari\n```\n\nIn napari, open **Plugins -> Guided Analysis** — the widget should load and show\nits Extract / Analyze / Statistics steps.\n\n### From source (development)\n\nFor working on the plugin itself, do Step 1 above, then install editable from a\ncheckout instead of from PyPI (`cd` into the plugin directory first, or pass the\nabsolute path):\n\n```powershell\nconda activate pentachrome\ncd \"...\\pentachrome_plugin\"\npip install -e .\n```\n\nThe three phase widgets are de-registered from the public menu. To open them\nstandalone during development, run the repo's `dev_widgets.py` — it docks the\nExtractor / Inference / Statistics widgets as tabs:\n\n```powershell\npython dev_widgets.py\n```\n\n## Launch\n\n```powershell\nconda activate pentachrome   # or whichever env you installed into\npython -m napari\n```\n\nIn napari: **Plugins -> Guided Analysis**.\n\n> The per-phase sections below (nnUNet Inference, Mask Statistics, etc.) describe\n> the underlying widgets, which the Guided Analysis pane drives end-to-end. Their\n> **Plugins -> ...** menu references apply only to the dev widgets opened via\n> `dev_widgets.py`; end users reach the same functionality through Guided Analysis.\n\n## Model weights\n\nThe nnUNet weights (~900 MB) aren't bundled in the PyPI package. Download\n[`nnunet_results.zip`]\nfrom the [releases page](https://github.com/dtsilis7/Pentrachrome-Pipeline/releases/tag/weights-v1),\nunzip it, and point the inference widget's **nnUNet results** field at the\nextracted folder (the one containing `Dataset001_Epithelium` and\n`Dataset002_MultiStructure`).\n\n## nnUNet Inference (Phase 2)\n\nRequires `nnunetv2` installed in the same environment (the `nnUNetv2_predict` CLI must be on PATH) — this is covered by Step 2 of [Installation](#installation-windows-powershell) above.\n\nWorkflow:\n\n1. Load TIFFs into napari (e.g. via Phase 1's auto-load checkbox, or drag-and-drop).\n2. Open **Plugins -> nnUNet Inference**.\n3. Select one or more image layers in the list.\n4. Tick **Epithelium**, **MultiStructure**, or both.\n5. Set **Output folder** (where raw + colorized masks go) and **nnUNet results** (folder containing `Dataset001_Epithelium` and `Dataset002_MultiStructure`). The results path auto-fills if `nnUNet_Training/nnUNet_results/results` is found.\n6. Pick **Device** (`cpu` or `cuda`) and click **Analyze**.\n\n### Speed vs quality (important on CPU)\n\nnnUNet inference on a laptop CPU is slow because every image goes through *folds × mirror augmentations × sliding-window patches* forward passes. With defaults that can be 20+ passes per image. The widget exposes three knobs in the **Speed / quality** group:\n\n| Knob | Default | What it does |\n| --- | --- | --- |\n| Epithelium folds | `Fold 0 only` | Use 1 of the 5 trained folds for Dataset001. All 5 ensembled is best quality but ~5x slower. Dataset002 only has fold 0 trained, so it's always 1 fold. |\n| Disable test-time mirroring | on | Passes `--disable_tta`. Skips the 4 mirror augmentations the model normally averages over. ~4x faster, small accuracy hit. |\n| Sliding-window step | `0.5` | Passes `-step_size`. Larger = fewer overlapping patches = faster but rougher tile borders. Try `0.7` for a middle ground. |\n\nWith all three defaults on a CPU laptop, one ROI tile should take a few minutes instead of 30+. Switch to `All 5 folds` + TTA on once you've moved to a GPU box.\n\n### Continuing from the extractor\n\nThe two widgets are linked through two small bridges, so you can run **Extract -> Analyze** in a single napari session without re-picking files:\n\n- When the extractor auto-loads a TIFF as a viewer layer, it stashes the on-disk path on `layer.metadata['source_tiff']`. The inference widget reads that during staging and **copies the original file** into `_staging_input/` rather than re-saving the in-memory array, important for 15k x 15k tiles.\n- When an extraction completes, the inference widget's **\"Use last extractor output\"** button pre-fills the output folder to `<extractor_output_root>/_inference`, so masks land next to the per-VSI subfolders the extractor created.\n\nBoth bridges are in-process only (see `_session.py`); they reset when napari closes.\n\nOutputs land in:\n\n```\n<output_folder>/\n  _staging_input/            # nnUNet-named (_0000.tif) copies of the selected layers\n  epithelium_raw/            # binary masks from Dataset001\n  epithelium_colored/        # RGB colorized masks (red epithelium)\n  multistructure_raw/        # 6-class masks from Dataset002\n  multistructure_colored/    # RGB colorized masks (Elastin/Collagen/Nuclei/Mucins/Membrane/Goblets)\n```\n\nColorized masks are added to the viewer as RGB image layers when the run finishes.\n\n### nnUNet inference architecture\n\nSame subprocess pattern as Phase 1. The widget never imports torch or nnUNetv2 directly; it spawns `_inference_worker.py` which:\n\n- sets `nnUNet_results` to the configured results dir,\n- calls `nnUNetv2_predict` once per enabled model (folds 0-4 for Epithelium, fold 0 for MultiStructure, matching `run_inference.py`),\n- colorizes the resulting integer masks with the palettes from `colorize_masks.py` / `compare_grid.py`,\n- streams JSON-line events on stdout for the widget's progress bar and log.\n\n## How it works\n\n- The widget itself never touches the JVM. When you click **Extract ROIs**, it spawns `_vsi_worker.py` as a separate Python process.\n- That worker process starts the bioformats JVM, loops over the VSI files using `TileMaskStitcher` (reused from `VSI_Handler/tile_mask_stitcher.py`), writes numbered TIFFs into `<output_root>/<vsi_basename>/`, and emits JSON-line progress events on stdout.\n- The widget streams those events on a background thread and updates the progress bar / log without blocking the UI.\n- When the worker exits, the JVM dies with it. The next extraction batch starts a fresh JVM in a fresh process - this avoids the \"JVM cannot be restarted\" pitfall during a long napari session.\n\n## Defaults\n\nThe parameter defaults mirror `Processing_VSI_Files.py`:\n\n| Parameter | Default |\n| --- | --- |\n| Series | 6 |\n| Tile width / height | 15000 |\n| Threshold | 50 |\n| Min ROI area | 150000 |\n| Merge margin | 1000 |\n| Extra crop margin | 100 |\n\n## Layout\n\n```\npentachrome_plugin/\n  pyproject.toml\n  README.md\n  src/pentachrome_plugin/\n    __init__.py\n    napari.yaml             # napari manifest\n    _session.py             # in-process cross-widget state (extractor -> inference -> analysis)\n    _widget.py              # VsiExtractorWidget (Phase 1)\n    _vsi_worker.py          # VSI subprocess entrypoint\n    _inference_widget.py    # NnUnetInferenceWidget (Phase 2)\n    _inference_worker.py    # nnUNet subprocess entrypoint\n    _analysis_widget.py     # AnalysisWidget (Phase 3, in-process)\n```\n\nPhase 3 (Mask Statistics) lives alongside these and registers through `napari.yaml`.\n\n## Mask Statistics (Phase 3)\n\nPure in-process; no subprocess needed (no JVM, no torch). Reuses\n`EpithelialAnalysis/Analyzers/` (`Descriptors.py`, `Thickness.py`), so the\nsame metrics that fed the original `region_summary.csv` show up in the\nwidget.\n\nWorkflow:\n\n1. Run Phase 2 first so `epithelium_raw/` and `multistructure_raw/` exist.\n2. Open **Plugins -> Mask Statistics**.\n3. Select one or more image layers in the list (their names must match the\n   mask filenames in `epithelium_raw/` / `multistructure_raw/`; if the\n   inference widget staged them, that's already true).\n4. Click **Use last inference output** (or browse).\n5. Tweak **Pixel size**, **Region dilation**, **Min epithelium area** if\n   needed (defaults match `Main.py`).\n6. Click **Analyze**.\n\nFor each detected epithelial region the widget reports:\n\n| Column | What it is |\n| --- | --- |\n| Area (mm^2) | Region area after the 50 um dilation |\n| Thickness mean/std (um) | Medial-axis thickness of (membrane within eroded region) U goblets U nuclei |\n| Elastin / Collagen / Other % | Fraction of stained structure pixels, same definition as `compute_structure_percentages` |\n| Mucin % | Mucin pixels as a fraction of the epithelium area (not of total structure pixels) |\n| Nuclei / mm^2 and Goblets / mm^2 | Density per mm^2 of epithelium, goblet hyperplasia is a classic COPD readout |\n| Nuclei (n), Goblets (n) | Raw counts inside the region |\n\nA bold **(all regions)** row appended per image gives area-weighted means\nof the percentages / thickness and totals for the counts. **Export CSV...**\nsaves the whole table (per-region rows + aggregate rows).\n\nThe elastin organization score (`ElastinAnalyzer.determine_organized_region`)\nfrom `Main.py` is intentionally not yet exposed, it's much heavier (skan +\nshapely + ROI polygons) and will land as a separate toggle.\n\n### Class isolation\n\nA \"Class isolation\" group at the top of the widget lets you view a single\nclass (or a combination) without rerunning anything:\n\n1. Pick a source layer (the **original** TIFF, not a colorized mask).\n2. Tick one or more of **Elastin**, **Collagen**, **Nuclei**, **Mucins**,\n   **Cell Membrane**, **Goblets**, **Epithelium**.\n3. Click one of:\n   - **Show as mask** — adds a new layer that's white everywhere except the\n     ticked classes, colored with the same palette as the inference widget.\n   - **Show on original** — adds a copy of the original image with all\n     pixels outside the ticked classes turned white. Useful for sanity-\n     checking the segmentation against the stain.\n4. **Clear isolated layers** removes everything this panel added in one go.\n\nMasks are read on demand from the inference output folder; the original\nlayer's pixels are taken from the viewer.\n\n## License\n\nThis project is licensed under the MIT License, see the [LICENSE](LICENSE) file for details.\n","description_content_type":"text/markdown","keywords":"napari,histology,nnunet,bioformats,segmentation","home_page":null,"download_url":null,"author":"Dimitrios Tsilis","author_email":null,"maintainer":null,"maintainer_email":null,"license":"MIT License\n\nCopyright (c) 2026 Dimitrios Tsilis\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.","classifier":["Framework :: napari","Development Status :: 4 - Beta","Intended Audience :: Science/Research","Programming Language :: Python :: 3.10","Operating System :: Microsoft :: Windows","License :: OSI Approved :: MIT License"],"requires_dist":["napari[all]>=0.4.18","qtpy","tifffile","numpy<2","opencv-python","scipy","scikit-image","skan","imagecodecs","matplotlib","shapely","pandas"],"requires_python":">=3.10","requires_external":null,"project_url":["Homepage, https://github.com/dtsilis7/Pentrachrome-Pipeline","Bug Tracker, https://github.com/dtsilis7/Pentrachrome-Pipeline/issues","Changelog, https://github.com/dtsilis7/Pentrachrome-Pipeline/blob/main/NapariInterface/pentachrome_plugin/CHANGELOG.md"],"provides_extra":null,"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}