annotate s3segmenter.xml @ 2:96d0d969ebc9 draft default tip

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/s3segmenter commit 0f4f17235c5961c2fd3d4c30180507f66214c11d

<tool id="s3segmenter" name="s3segmenter" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>single cell (nuclei and cytoplasm) label masks.</description>
    <macros>
        <import>macros.xml</import>
    </macros>
 
    <expand macro="requirements"/>
    <expand macro="version_cmd"/>

    <command detect_errors="exit_code"><![CDATA[

        ln -s '${imagePath}' './image.tif' &&

        #if $contoursClassProbPath
        ln -s '${contoursClassProbPath}' './ContoursPM.tif' &&
        #end if

        #if $nucleiClassProbPath
        ln -s '${nucleiClassProbPath}' './NucleiPM.tif' &&
        #end if

        #if $stackProbPath
        ln -s '${stackProbPath}' './Probabilities.tif' &&
        #end if


        @CMD_BEGIN@ \$S3SEG_CMD 
        --imagePath './image.tif'

        #if $contoursClassProbPath
        --contoursClassProbPath './ContoursPM.tif'
        #end if

        #if $nucleiClassProbPath
        --nucleiClassProbPath './NucleiPM.tif'
        #end if

        #if $stackProbPath
        --stackProbPath './Probabilities.tif'
        #end if

        --probMapChan $probMapChan
        --crop $crop_select.crop
        
        #if $crop_select.crop == "dearray"
          --maskPath '$crop_select.maskPath'
        #end if

        --cytoMethod $cytoMethod
        --nucleiFilter $nucleiFilter
        --nucleiRegion $nucleiRegion_select.nucleiRegion

        #if $nucleiRegion_select.nucleiRegion == "pixellevel"
          --pixelThreshold $nucleiRegion_select.pixelThreshold
          --pixelMaskChan $nucleiRegion_select.pixelMaskChan
        #end if

        --segmentCytoplasm $segmentCytoplasm
        --cytoDilation $adv.cytoDilation
        --logSigma $adv.logSigma
        --CytoMaskChan $adv.CytoMaskChan
        ##--TissueMaskChan $adv.TissueMaskChan
        --detectPuncta $adv.detectPuncta
        --punctaSigma $adv.punctaSigma
        --punctaSD $adv.punctaSD
        $saveMask
        $saveFig
        --outputPath '.'
    ]]></command>


    <inputs>

        <param name="imagePath" type="data" format="tiff" label="Image File"/>
        <param name="contoursClassProbPath" type="data" format="tiff" optional="true" label="Contours Class Probabilities"/>
        <param name="nucleiClassProbPath" type="data" format="tiff" optional="true" label="Nuclei Class Probabilities"/>
        <param name="stackProbPath" type="data" format="tiff" optional="true" label="Stack Probabilities"/>
        <param name="probMapChan" type="integer" value="-1" label="Probability Maps Channel"/>

        <conditional name="crop_select">
            <param name="crop" type="select" label="Crop Strategy">
                <option selected="true" value="noCrop">No Crop</option>
                <option value="autoCrop">Automatic Crop</option>
                <option value="dearray">De-array</option>
                <option value="plate">Plate</option>
            </param>
            <when value="dearray">
                <param name="maskPath" type="data" format="tiff" label="TMA Mask File"/>
            </when>
            <when value="noCrop" />
            <when value="autoCrop" />
            <when value="plate" />
        </conditional>

        <param name="cytoMethod" type="select" label="Cyto Method">
            <option value="hybrid">Hybrid</option>
            <option selected="true" value="distanceTransform">Distance Transform</option>
            <option value="bwdistanceTransform">BW Distance Transform</option>
            <option value="ring">Ring</option>
        </param>
        <param name="nucleiFilter" type="select" label="Nuclei Filter">
            <option selected="true" value="IntPM">IntPM</option>
            <option value="LoG">LoG</option>
            <option value="Int">Int</option>
            <option value="none">none</option>
        </param>

        <conditional name="nucleiRegion_select">
            <param name="nucleiRegion" type="select" label="Nuclei Region">
                <option value="watershedContourDist">watershedContourDist</option>
                <option selected="true" value="watershedContourInt">watershedContourInt</option>
                <option value="watershedBWDist">watershedBWDist</option>
                <option value="dilation">dilation</option>
                <option value="localThreshold">localThreshold</option>
                <option value="localMax">localMax</option>
                <option value="bypass">bypass</option>
                <option value="pixellevel">pixellevel</option>
            </param>
            <when value="pixellevel">
                <param name="pixelThreshold" type="float" value="-1.0" label="Pixel Threshold"/>
                <param name="pixelMaskChan" type="text" value="2" label="Pixel Mask Channel"/>
            </when>
             <when value="watershedContourDist"/>
             <when value="watershedContourInt"/>
             <when value="watershedBWDist"/>
             <when value="dilation"/>
             <when value="localThreshold"/>
             <when value="localMax"/>
             <when value="bypass"/>
        </conditional>

        <param name="segmentCytoplasm" type="select" label="Segment Cytoplasm">
            <option value="segmentCytoplasm">segmentCytoplasm</option>
            <option selected="true" value="ignoreCytoplasm">ignoreCytoplasm</option>
        </param>
        <param argument="saveMask" type="boolean" checked="true" truevalue="--saveMask" falsevalue="" label="Save Mask"/>
        <param argument="saveFig" type="boolean" checked="true" truevalue="--saveFig" falsevalue="" label="Save Figure"/>

        <section name="adv" title="Advanced Options" expanded="false">
            <param name="cytoDilation" type="integer" value="5" label="Cyto Dilation"/>
            <param name="logSigma" type="text" value="3 60" label="logSigma"/>
            <param name="CytoMaskChan" type="text" value="2" label="Cyto Mask Channel"/>
            <!-- Bug with S3Segmenter code, expects int not list
            <param name="TissueMaskChan" type="text" value="-1" label="Tissue Mask Channel"/>
            -->
            <param name="detectPuncta" type="text" value="-1" label="Detect Puncta"/>
            <param name="punctaSigma" type="text" value="1" label="Puncta Sigma"/>
            <param name="punctaSD" type="text" value="4" label="Puncta SD"/>
        </section>
        

    </inputs>
    <outputs>
        <data format="tiff" name="cell_mask" from_work_dir="image/cell.ome.tif" label="cellMasks">
            <filter>saveMask is True</filter>
        </data>
        <data format="tiff" name="nuclei_mask" from_work_dir="image/nuclei.ome.tif" label="nucleiMasks">
            <filter>saveMask is True</filter>
        </data>
	<data format="tiff" name="cell_outlines" from_work_dir="image/qc/cellOutlines.ome.tif" label="cellOutlines">
            <filter>saveFig is True</filter>
        </data>
	<data format="tiff" name="nuclei_outlines" from_work_dir="image/qc/nucleiOutlines.ome.tif" label="nucleiOutlines">
            <filter>saveFig is True</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="imagePath" value="test.ome.tiff" />
            <param name="stackProbPath" value="stack_probabilities.tiff" />
            <param name="punctaSD" value="4" />
            <output name="cell_mask" ftype="tiff">
                <assert_contents>
                    <has_size value="6600000" delta="1000000" />
                </assert_contents>
            </output>
            <output name="nuclei_mask" ftype="tiff">
                <assert_contents>
                    <has_size value="6600000" delta="1000000" />
                </assert_contents>
            </output>
        </test>
    </tests>
    <help><![CDATA[
-------------------        
S3segmenter
-------------------        
**S3segmenter** is a Python-based set of functions that generates single cell (nuclei and cytoplasm) label masks.

Inputs are:

1. an .ome.tif (preferably flat field corrected)
2. a 3-class probability maps derived from a deep learning model such as UNet. Classes include background, nuclei contours, and nuclei foreground.

The centers of each nuclei are obtained by finding local maxima from the nuclei foreground. These are used for marker-controlled watershed constrained by the nuclei contours.

To segment cytoplasm, the nuclei are in turn used for a marker-controlled watershed segmentation constrained by a cytoplasmic marker such as B-catenin. The channel number of this marker must be specified. A 3-pixel annulus around each nucleus will also be used to segment cytoplasm.

The source repository can be found here: https://github.com/HMS-IDAC/S3segmenter
    ]]></help>
    <expand macro="citations" />
</tool>