diff unmicst.xml @ 0:6bec4fef6b2e draft

"planemo upload for repository https://github.com/ohsu-comp-bio/unmicst commit 73e4cae15f2d7cdc86719e77470eb00af4b6ebb7-dirty"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/unmicst.xml	Fri Mar 12 00:17:29 2021 +0000
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+<tool id="unmicst" name="UnMicst" version="@VERSION@.1" profile="17.09">
+    <description>UNet Model for Identifying Cells and Segmenting Tissue</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+ 
+    <expand macro="requirements"/>
+    @VERSION_CMD@
+
+    <command detect_errors="exit_code"><![CDATA[
+    #set $typeCorrected = str($image.name).replace('.ome.tiff','').replace('.ome.tif','').replace('.tiff','').replace('.tif','')+'.ome.tif'
+
+    ln -s $image '$typeCorrected';
+
+    @CMD_BEGIN@ '$typeCorrected'
+    
+    #if $stackoutput
+    --stackOutput
+    #end if
+
+    --outputPath `pwd`
+    --channel $channel
+    --model $model
+    --mean $mean
+    --std $stdev
+    --scalingFactor $scalingfactor;
+
+    ## Move files to different files for from_work_dir differentiation
+    #if $stackoutput
+    mv *Probabilities*.tif Probabilities.tif;
+    mv *Preview*.tif Preview.tif
+    #else
+    mv *ContoursPM*.tif ContoursPM.tif;
+    mv *NucleiPM*.tif NucleiPM.tif
+    #end if
+    ]]></command>
+
+    <inputs>
+        <param name="image" type="data" format="tiff" label="Registered TIFF"/>
+        <param name="model" type="select" label="Model">
+            <option value="nucleiDAPI">nucleiDAPI</option>
+            <option value="mousenucleiDAPI">mousenucleiDAPI</option>
+            <option value="CytoplasmIncell">CytoplasmIncell</option>
+            <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option>
+        </param>
+        <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/>
+        <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/>
+        <param name="channel" type="integer" value="0" label="Channel to perform inference on"/>
+        <param name="stackoutput" type="boolean"  label="Stack probability map outputs"/>
+        <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/>
+    </inputs>
+
+    <outputs>
+        <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview">
+            <filter>stackoutput</filter>
+        </data>
+        <data format="tiff" name="probabilities" from_work_dir="Probabilities.tif" label="${tool.name} on ${on_string}: Probabilities">
+            <filter>stackoutput</filter>
+        </data>
+        <data format="tiff" name="contours" from_work_dir="ContoursPM.tif" label="${tool.name} on ${on_string}: ContoursPM">
+            <filter>not stackoutput</filter>
+        </data>
+        <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM">
+            <filter>not stackoutput</filter>
+        </data>
+    </outputs>
+    <help><![CDATA[
+UnMicst - UNet Model for Identifying Cells and Segmenting Tissue
+Image Preprocessing
+Images can be preprocessed by inferring nuclei contours via a pretrained UNet model. The model is trained on 3 classes : background, nuclei contours and nuclei centers. The resulting probability maps can then be loaded into any modular segmentation pipeline that may use (but not limited to) a marker controlled watershed algorithm.
+
+The only input file is: an .ome.tif or .tif (preferably flat field corrected, minimal saturated pixels, and in focus. The model is trained on images acquired at 20x with binning 2x2 or a pixel size of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent.
+
+Running as a Docker container
+
+The docker image is distributed through Dockerhub and includes UnMicst with all of its dependencies. Parallel images with and without gpu support are available.
+
+docker pull labsyspharm/unmicst:latest
+docker pull labsyspharm/unmicst:latest-gpu
+Instatiate a container and mount the input directory containing your image.
+
+docker run -it --runtime=nvidia -v /path/to/data:/data labsyspharm/unmicst:latest-gpu bash
+When using the CPU-only image, --runtime=nvidia can be omitted:
+
+docker run -it -v /path/to/data:/data labsyspharm/unmicst:latest bash
+UnMicst resides in the /app directory inside the container:
+
+root@0ea0cdc46c8f:/# python app/UnMicst.py /data/input/my.tif --outputPath /data/results
+Running in a Conda environment
+
+If Docker is not available on your system, you can run the tool locally by creating a Conda environment. Ensure conda is installed on your system, then clone the repo and use conda.yml to create the environment.
+
+git clone https://github.com/HMS-IDAC/UnMicst.git
+cd UnMicst
+conda env create -f conda.yml
+conda activate unmicst
+python UnMicst.py /path/to/input.tif --outputPath /path/to/results/directory
+References:
+S Saka, Y Wang, J Kishi, A Zhu, Y Zeng, W Xie, K Kirli, C Yapp, M Cicconet, BJ Beliveau, SW Lapan, S Yin, M Lin, E Boyde, PS Kaeser, G Pihan, GM Church, P Yin, Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER, Nat Biotechnology (accepted)
+
+OHSU Wrapper Repo: https://github.com/ohsu-comp-bio/UnMicst
+    ]]></help>
+    <expand macro="citations" />
+</tool>