New tutorial: Quantitative Analysis of Histological Staining Using Color Deconvolution#6773
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| {"a_galaxy_workflow": "true", "annotation": "Colour deconvolution-based workflow for staining area quantification in brightfield histological images (MT, IHC). Accepts whole slides or pre-cropped ROIs.", "comments": [{"child_steps": [11], "color": "black", "data": {"title": "Stained Area Detection"}, "id": 2, "position": [1102.7, 1.5], "size": [290, 501], "type": "frame"}, {"child_steps": [2, 4, 12, 24, 6, 7, 9, 10, 14, 18, 21], "color": "none", "data": {"title": "Total Area Computing"}, "id": 1, "position": [0.7999999999999999, 555.8000000000001], "size": [2789.4, 503], "type": "frame"}, {"child_steps": [3, 5, 8, 0], "color": "turquoise", "data": {"title": "Image Preparation"}, "id": 0, "position": [0, 0], "size": [1084, 502], "type": "frame"}, {"child_steps": [25, 26, 27], "color": "pink", "data": {"title": "Final Results - Tabular File"}, "id": 4, "position": [2841.7000000000003, 227.1], "size": [809, 490], "type": "frame"}, {"child_steps": [13, 15, 22, 23, 16, 17, 19, 20, 1], "color": "green", "data": {"title": "Quantification and Sanitation Steps for Outfile"}, "id": 3, "position": [1408, 1.9000000000000004], "size": [1377, 501], "type": "frame"}], "creator": [{"class": "Person", "identifier": "0000-0002-5857-1477", "name": "Diana Chiang Jurado"}], "format-version": "0.1", "license": "CC-BY-4.0", "name": "Histological Staining Area Quantification", "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- `area` \u2014 total number of stained pixels\n- `mean_intensity` \u2014 mean pixel intensity within the stained region\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.", "report": {"markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n"}, "steps": {"0": {"annotation": "Brightfield histological image (whole slide or pre-cropped ROI) for staining quantification. Compatible stainings include Masson's Trichrome and IHC. 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Channel 1 = Hematoxylin, Channel 2 = Eosin/target stain, Channel 3 = Residuals. 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Extracts the target stain channel from the split image collection. Default index: 1 (Channel 2). Verify this corresponds to your stain of interest before running the workflow. 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Please verify the selected channel is correct before proceeding. The current index is set to extract dataset 2.", "content_id": "__EXTRACT_DATASET__", "errors": null, "id": 8, "input_connections": {"input": {"id": 5, "output_name": "output"}}, "inputs": [], "label": "Collection: Extract Stain Channel from Sub-Collections", "name": "Extract dataset", "outputs": [{"name": "output", "type": "data"}], "position": {"left": 836.1708176873433, "top": 54.84974208436942}, "post_job_actions": {"ChangeDatatypeActionoutput": {"action_arguments": {"newtype": "tiff"}, "action_type": "ChangeDatatypeAction", "output_name": "output"}}, "tool_id": "__EXTRACT_DATASET__", "tool_state": "{\"input\": {\"__class__\": \"ConnectedValue\"}, \"which\": {\"which_dataset\": \"by_index\", \"__current_case__\": 2, \"index\": \"1\"}, \"__page__\": 0, \"__rerun_remap_job_id__\": null}", "tool_uuid": null, "tool_version": "1.0.2", "type": "tool", "uuid": "01518895-46e2-4ec3-aa0e-1898357255fa", "when": null, "workflow_outputs": [{"label": "Selected Stain Channel", "output_name": "output", "uuid": "23ea738b-06ae-4688-ab4a-b29c196a9cfa"}]}, "9": {"annotation": "", "content_id": "toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sed_tool/9.5+galaxy3", "errors": null, "id": 9, "input_connections": {"infile": {"id": 6, "output_name": "out_file1"}}, "inputs": [], "label": null, "name": "Text transformation", "outputs": [{"name": "output", "type": "input"}], "position": {"left": 953.4203075074834, "top": 651.8928760243856}, "post_job_actions": {}, "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sed_tool/9.5+galaxy3", "tool_shed_repository": {"changeset_revision": "ab83aa685821", "name": "text_processing", "owner": "bgruening", "tool_shed": "toolshed.g2.bx.psu.edu"}, "tool_state": "{\"adv_opts\": {\"adv_opts_selector\": \"basic\", \"__current_case__\": 0}, \"code\": \"s/.*= //\", \"infile\": {\"__class__\": \"ConnectedValue\"}, \"__page__\": 0, \"__rerun_remap_job_id__\": null}", "tool_uuid": null, "tool_version": "9.5+galaxy3", "type": "tool", "uuid": "5f158fe1-70f9-4c38-806c-88417993a83b", "when": null, "workflow_outputs": []}, "10": {"annotation": "", "content_id": "toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sed_tool/9.5+galaxy3", "errors": null, "id": 10, "input_connections": {"infile": {"id": 7, "output_name": "out_file1"}}, "inputs": [], "label": null, "name": "Text transformation", "outputs": [{"name": "output", "type": "input"}], "position": {"left": 950.8939905212409, "top": 891.2273472144357}, "post_job_actions": {}, "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sed_tool/9.5+galaxy3", "tool_shed_repository": {"changeset_revision": "ab83aa685821", "name": "text_processing", "owner": "bgruening", "tool_shed": "toolshed.g2.bx.psu.edu"}, "tool_state": "{\"adv_opts\": {\"adv_opts_selector\": \"basic\", \"__current_case__\": 0}, \"code\": \"s/.*= //\", \"infile\": {\"__class__\": \"ConnectedValue\"}, \"__page__\": 0, \"__rerun_remap_job_id__\": null}", "tool_uuid": null, "tool_version": "9.5+galaxy3", "type": "tool", "uuid": "3023c1cf-e64e-49d9-9238-1e4020304824", "when": null, "workflow_outputs": []}, "11": {"annotation": "Applies a threshold to the selected stain channel to segment stained areas for quantification.\nTool only process tiff files with 1 channel. 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"name:HistologyStaining", "name:MassonTrichrome", "name:Immunohistochemistry", "name:BrightfieldMicroscopy", "name:ColorDeconvolution"], "uuid": "218d214d-1142-4888-914b-9108e8f9f253", "version": 41} No newline at end of file | |||
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🚫 [GTN Lint] <GTN:027> reported by reviewdog 🐶
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| url = {https://pubmed.ncbi.nlm.nih.gov/11531144} | ||
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| Color deconvolution solves this by using a **stain matrix**, a set of stain vectors that describe the optical density contributions of each stain across the three RGB channels. The algorithm inverts this matrix to compute the individual optical density of each stain at every pixel, producing a separate grayscale image per stain channel. | ||
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| **Common stain presets available in Galaxy:** |
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| **Common stain presets available in Galaxy:** | |
| **### Common stain presets available in Galaxy:** |
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This PR adds a new hands-on tutorial for the Imaging topic demonstrating how to quantify
the percentage of stained area in histological images using color deconvolution in Galaxy.
File location:
topics/imaging/tutorials/stain-quantification-color-deconvolution/What this tutorial covers
Status: Draft — pending before review
zenodo_linktopics/imaging/images/stain-quantification-color-deconvolution/👋 @afgane