Cwl Segmentation workflow

README.md

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+# Common Workflow Language (CWL) Nuclear Segmentation worflow
+
+CWL feature extraction workflow for imaging dataset
+
+##  Workflow Steps:
+
+create a [Conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment) environment using python = ">=3.9,<3.12"
+
+#### 1. Install polus-plugins.
+
+- clone a image-tools reporsitory
+`git clone https://github.com/camilovelezr/image-tools.git`
+- cd `image-tools`
+- `pip install .`
+
+#### 2. Install workflow-inference-compiler.
+- clone a workflow-inference-compiler reporsitory
+`git clone https://github.com/camilovelezr/workflow-inference-compiler.git`
+- cd `workflow-inference-compiler`
+- `pip install -e ".[all]"`
+
+## Details 
+This workflow integrates seven distinct plugins, starting from data retrieval from [Broad Bioimage Benchmark Collection](https://bbbc.broadinstitute.org/), renaming files, correcting uneven illumination, segmenting nuclear objects.
+
+Below are the specifics of the plugins employed in the workflow
+1. [bbbc-download-plugin](https://github.com/saketprem/polus-plugins/tree/bbbc_download/utils/bbbc-download-plugin)
+2. [file-renaming-tool](https://github.com/PolusAI/image-tools/tree/master/formats/file-renaming-tool)
+3. [ome-converter-tool](https://github.com/PolusAI/image-tools/tree/master/formats/ome-converter-tool)
+4. [basic-flatfield-estimation-tool](https://github.com/PolusAI/image-tools/tree/master/regression/basic-flatfield-estimation-tool)
+5. [apply-flatfield-tool](https://github.com/PolusAI/image-tools/tree/master/transforms/images/apply-flatfield-tool)
+6. [kaggle-nuclei-segmentation](https://github.com/hamshkhawar/image-tools/tree/kaggle-nuclei_seg/segmentation/kaggle-nuclei-segmentation)
+7. [polus-ftl-label-plugin](https://github.com/hamshkhawar/image-tools/tree/kaggle-nuclei_seg/transforms/images/polus-ftl-label-plugin)
+
+## Execute CWL Segmentation workflow
+
+The parameters for each imaging dataset are pre-defined and stored in JSON format. A Pydantic model in a utils Python file can be utilized to store parameters for any new dataset
+
+`python cwl_workflows/__main__.py --name="BBBC039" --workflow=segmentation`
+
+A directory named `workflow` is generated, encompassing CLTs for each plugin, YAML files, and all outputs are stored within the `outdir` directory.
+```
+workflows
+├── experiment
+│   └── cwl_adapters
+|   experiment.cwl
+|   experiment.yml
+|
+└── outdir
+    └── experiment
+        ├── step 1 BbbcDownload
+        │   └── outDir
+        │       └── bbbc.outDir
+        │           └── BBBC
+        │               └── BBBC039
+        │                   └── raw
+        │                       ├── Ground_Truth
+        │                       │   ├── masks
+        │                       │   └── metadata
+        │                       └── Images
+        │                           └── images
+        ├── step 2 FileRenaming
+        │   └── outDir
+        │       └── rename.outDir
+        ├── step 3 OmeConverter
+        │   └── outDir
+        │       └── ome_converter.outDir
+        ├── step 4 BasicFlatfieldEstimation
+        │   └── outDir
+        │       └── estimate_flatfield.outDir
+        ├── step 5 ApplyFlatfield
+        │   └── outDir
+        │       └── apply_flatfield.outDir
+        ├── step 6 KaggleNucleiSegmentation
+        │   └── outDir
+        │       └── kaggle_nuclei_segmentation.outDir
+        ├── step 7 FtlLabel
+        │   └── outDir
+        │       └── ftl_plugin.outDir
+
+```

bbbc_json/bbbc_segmentation.json

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+{
+  "data": {
+    "BBBC039": {
+      "name": "BBBC039",
+      "file_pattern": ".*_{row:c}{col:dd}_s{s:d}_w{channel:d}.*.tif",
+      "out_file_pattern": "x{row:dd}_y{col:dd}_p{s:dd}_c{channel:d}.tif",
+      "image_pattern": "images_x{x:dd}_y{y:dd}_p{p:dd}_c{c:d}.ome.tif",
+      "seg_pattern": "images_x{x:dd}_y{y:dd}_p{p:dd}_c1.ome.tif",
+      "map_directory": "raw",
+      "ff_pattern": "images_x\\(00-15\\)_y\\(01-24\\)_p0\\(1-9\\)_c{c:d}_flatfield.ome.tif",
+      "df_pattern": "images_x\\(00-15\\)_y\\(01-24\\)_p0\\(1-9\\)_c{c:d}_darkfield.ome.tif",
+      "group_by": "c"
+    }
+  }
+}