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Updating workflows/imaging/histological-staining-area-quantification from 0.1.1 to 0.1.2 #1250

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7 changes: 7 additions & 0 deletions workflows/imaging/histological-staining-area-quantification/CHANGELOG.md
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# Changelog

## [0.1.2] - 2026-06-01

### Automatic update
- `toolshed.g2.bx.psu.edu/repos/imgteam/image_info/ip_imageinfo/5.7.1+galaxy1` was updated to `toolshed.g2.bx.psu.edu/repos/imgteam/image_info/ip_imageinfo/8.0.0+galaxy0`
- `toolshed.g2.bx.psu.edu/repos/imgteam/split_image/ip_split_image/2.3.5+galaxy0` was updated to `toolshed.g2.bx.psu.edu/repos/imgteam/split_image/ip_split_image/2.3.5+galaxy1`
- `toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.2` was updated to `toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.3`

## [0.1.1] - 2026-05-28

### Fixed
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45 changes: 20 additions & 25 deletions ...ng/histological-staining-area-quantification/histological-staining-area-quantification.ga
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"readme": "# Histological Staining Area Quantification\n\n## Overview\nThis workflow quantifies stained tissue areas in brightfield histological images \nusing colour deconvolution and automated thresholding. It is designed for \n3-channel stainings compatible with the H-E-DAB (HED) colour space, such as \nMasson's Trichrome (MT) and Immunohistochemistry (IHC).\n\n## What does this workflow do?\n1. **Colour deconvolution** separates the input image into individual stain channels (HED).\n2. **Channel splitting and extraction** isolates the target stain channel (default: Channel 2).\n3. **Automated thresholding** (Li method) generates a binary mask of stained vs. unstained regions.\n4. **Feature extraction** quantifies staining area and intensity per sample.\n5. **Output collation** merges all per-sample results into a single tabular file.\n\n## Input data requirements\n- **Format:** TIFF\n- **Type:** Brightfield microscopy images\n- **Staining:** Compatible with HED colour deconvolution (e.g., Masson's Trichrome, IHC)\n- **Content:** Whole slide images or pre-cropped regions of interest (ROIs)\n- **Structure:** Dataset collection (list) \u2014 one image per sample\n\n> \u26a0\ufe0f Images must be pre-cropped to the region of interest before use.\n> The workflow does not perform ROI selection.\n\n## Output\nA tabular file (TSV) containing one row per sample with the following columns:\n- `sample_id` \u2014 sample identifier\n- `label` \u2014 pixel label (1 = stained region)\n- `mean_intensity` \u2014 mean pixel intensity within the stained region\n- `area` \u2014 total number of stained pixels\n- `area_filled` \u2014 stained area with internal holes filled\n- `total_area` \u2014 total pixel count of the input image (width \u00d7 height)\n- `percent_area` \u2014 staining area as percentage of total image area\n\n## Important notes\n- Verify that **Channel 2** corresponds to your stain of interest before running.\n This can be checked by inspecting the deconvolved image output.\n- The thresholding method is **Li**. If results are suboptimal, adjust the offset \n in the Threshold step.\n- The workflow processes images as a **collection**, enabling batch analysis \n of multiple samples in a single run.\n\n## Compatible stainings\n| Staining | Target channel |\n|----------|---------------|\n| Masson's Trichrome (MT) | Channel 2 (Eosin/collagen) |\n| Immunohistochemistry (IHC) | Channel 2 (DAB) |\n\n## Citation\nIf you use this workflow, please cite it via its WorkflowHub DOI.",
"readme": "# Histological Staining Area Quantification\n\n## Overview\nThis workflow quantifies stained tissue areas in brightfield histological images \nusing colour deconvolution and automated thresholding. It is designed for \n3-channel stainings compatible with the H-E-DAB (HED) colour space, such as \nMasson's Trichrome (MT) and Immunohistochemistry (IHC).\n\n## What does this workflow do?\n1. **Colour deconvolution** separates the input image into individual stain channels (HED).\n2. **Channel splitting and extraction** isolates the target stain channel (default: Channel 2).\n3. **Automated thresholding** (Li method) generates a binary mask of stained vs. unstained regions.\n4. **Feature extraction** quantifies staining area and intensity per sample.\n5. **Output collation** merges all per-sample results into a single tabular file.\n\n## Input data requirements\n- **Format:** TIFF\n- **Type:** Brightfield microscopy images\n- **Staining:** Compatible with HED colour deconvolution (e.g., Masson's Trichrome, IHC)\n- **Content:** Whole slide images or pre-cropped regions of interest (ROIs)\n- **Structure:** Dataset collection (list) — one image per sample\n\n> ⚠️ Images must be pre-cropped to the region of interest before use.\n> The workflow does not perform ROI selection.\n\n## Output\nA tabular file (TSV) containing one row per sample with the following columns:\n- `sample_id` — sample identifier\n- `label` — pixel label (1 = stained region)\n- `mean_intensity` — mean pixel intensity within the stained region\n- `area` — total number of stained pixels\n- `area_filled` — stained area with internal holes filled\n- `total_area` — total pixel count of the input image (width × height)\n- `percent_area` — staining area as percentage of total image area\n\n## Important notes\n- Verify that **Channel 2** corresponds to your stain of interest before running.\n This can be checked by inspecting the deconvolved image output.\n- The thresholding method is **Li**. If results are suboptimal, adjust the offset \n in the Threshold step.\n- The workflow processes images as a **collection**, enabling batch analysis \n of multiple samples in a single run.\n\n## Compatible stainings\n| Staining | Target channel |\n|----------|---------------|\n| Masson's Trichrome (MT) | Channel 2 (Eosin/collagen) |\n| Immunohistochemistry (IHC) | Channel 2 (DAB) |\n\n## Citation\nIf you use this workflow, please cite it via its WorkflowHub DOI.",
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"markdown": "# Histological Staining Area Quantification\n\n```galaxy\ninvocation_time()\n```\n\n---\n\n## Summary\n\nThis workflow is designed to quantify the area and intensity of a specific stain in brightfield histological images. It takes a collection of input images, applies H-E-DAB colour deconvolution to separate the target stain channel from the haematoxylin counterstain, then uses automated thresholding to segment positive-staining pixels. Per-sample measurements are collated into a single tabular output.\n\nCompatible staining types include immunohistochemistry (IHC) with a DAB chromogen and Masson's Trichrome (MT). The workflow expects RGB TIFF images — one region of interest (ROI) per collection element.\n\n```galaxy\nworkflow_image()\n```\n\n---\n\n## Input Images\n\nThe input to this workflow is a list collection of brightfield microscopy images. Each element should represent one region of interest from a tissue section. The images provided for this run are shown below.\n\n```galaxy\nvisualization(visualization_id=tiffviewer, input=\"ROI image for staining analysis\")\n```\n\n---\n\n## Staining Mask\n\nIf the run completed successfully, each input image will have been converted into a binary mask via colour deconvolution and automated thresholding. In this mask, **white pixels** represent regions classified as positively stained and **black pixels** represent background. This mask is what the quantification measurements are derived from.\n\n```galaxy\nvisualization(visualization_id=tiffviewer, output=\"Selected Stain Channel Thresholded\")\n```\n\n---\n\n## Results\n\nIf the workflow completed successfully, the table below shows per-sample staining measurements — one row per input image. The expected columns are described below.\n\n| Column | Description |\n|--------|-------------|\n| `sample_id` | Identifier derived from the input image filename |\n| `label` | Region label assigned by the thresholding step |\n| `mean_intensity` | Average pixel intensity within the detected region (lower = stronger stain, due to deconvolution inversion) |\n| `area` | Pixel count of the positively stained region |\n| `area_filled` | Stained area with internal holes filled |\n| `total_area` | Total pixel count of the input image (width × height) |\n| `percent_area` | Staining area as a percentage of total image area (`area / total_area × 100`) |\n\n```galaxy\nhistory_dataset_as_table(output=\"Tabular File: Staining Feature Results\", title=\"Staining Feature Results\")\n```\n\n```galaxy\nhistory_dataset_link(output=\"Tabular File: Staining Feature Results\", label=\"Download results (TSV)\")\n```\n\n---\n\n## Reproducibility\n\n```galaxy\nhistory_link()\n```\n\n```galaxy\nworkflow_license()\n```\n"
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