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annotate mesmer.xml @ 3:c60b810d570d draft

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit caf99c99d5698c3524e6eb7fd43c0197c2a3b3cb
author goeckslab
date Tue, 28 Nov 2023 19:46:58 +0000
parents 187918c47051
children 2675ff99a53b
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53240d7c1fc5 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 5fb6aa9066c68d9972b3cc4be13503a952f952a5
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1 <tool id="mesmer" name="Mesmer" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
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2 <description>Mesmer for whole-cell segmentation of multiplexed tissue imaging data</description>
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3
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4 <macros>
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5 <import>macros.xml</import>
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6 </macros>
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8 <expand macro="requirements"/>
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9 <expand macro="stdio"/>
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11 <command detect_errors="exit_code"><![CDATA[
2
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12 python $script
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13 ]]></command>
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14 <configfiles>
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15 <configfile name="script">
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16 import argparse
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17 import os
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18 import sys
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20 import numpy as np
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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21 import tifffile
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22 import zarr
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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24 from deepcell.applications import Mesmer
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26 level = 0
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27 is_ome=False
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28 #if $image.file_ext == "ome.tiff":
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29 is_ome=True
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30 #end if
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32 ## Grab params
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33 nuc_kwargs = {}
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34 wc_kwargs = {}
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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37 with tifffile.TiffFile("$image", is_ome=is_ome) as tiff:
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39 # Read single pyramid level
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40 level_array = zarr.open(tiff.aszarr(series = 0, level = level))
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42 ## grab the nuclear and membrane channels based on their indices add the markers along the channel axis
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43 ## Tifffile should always read as (channel,X,Y)
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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44 #if $compartment_select.compartment != 'whole-cell':
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45 nuc_kwargs = {
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46 #if $compartment_select.nuclear_options.pixel_expansion != '':
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47 'pixel_expansion': $compartment_select.nuclear_options.pixel_expansion,
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48 #end if
3
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49 #for $key, $value in $compartment_select.nuclear_options.adv_options.items():
2
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50 '$key': $value,
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51 #end for
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52 'maxima_threshold': $compartment_select.nuclear_options.maxima_threshold
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53 }
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54 #end if
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55
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56 nuclear_indices = [int(x) for x in $nuclear_channels.split(',')]
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57 nuclear_channels = level_array.oindex[nuclear_indices, :, :]
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58 nuclear_channels = np.sum(nuclear_channels, axis = 0)
0
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59
2
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60 #if $compartment_select.compartment != 'nuclear':
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61 wc_kwargs = {
3
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62 #for $key, $value in $compartment_select.wc_options.adv_options.items():
2
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63 '$key': $value,
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64 #end for
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65 'maxima_threshold': $compartment_select.wc_options.maxima_threshold
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66 }
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67
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68 membrane_indices = [int(x) for x in $compartment_select.wc_options.wc_channels.split(',')]
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69 membrane_channels = level_array.oindex[membrane_indices, :, :]
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70 membrane_channels = np.sum(membrane_channels, axis = 0)
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71 #end if
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72
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73 ## stack the nuclear and membrane composite channels with nuclear in channel 0 and mem in channel 1
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74 ## mesmer expects dimensions to be (X,Y,Channel) so axis = -1
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75 #if $compartment_select.compartment == 'nuclear':
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76 membrane_channels = np.zeros(nuclear_channels.shape)
0
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77 #end if
2
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78 formatted_image = np.stack((nuclear_channels,membrane_channels), axis=-1)
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79
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80 ## add batch dimension. Will have to be squeezed out later
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81 formatted_image = np.expand_dims(formatted_image, 0)
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82
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83 ## Create the application
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84 app = Mesmer()
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85
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86 ## Run segmentation
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87 mask = app.predict(
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88 formatted_image,
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89 image_mpp = $image_mpp,
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90 compartment = "$compartment_select.compartment",
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91 pad_mode = 'constant',
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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92 postprocess_kwargs_whole_cell = wc_kwargs,
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93 postprocess_kwargs_nuclear = nuc_kwargs)
0
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94
2
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95 #if $squeeze:
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96 mask = np.squeeze(mask)
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97 #end if
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98
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99 #if $compartment_select.compartment == 'both':
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100 ## split the two-channel mask into separate outputs
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101 #if $squeeze:
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102 tifffile.imsave( "WC_output_mask.tif", mask[:,:,0])
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103 tifffile.imsave("NU_output_mask.tif", mask[:,:,1])
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104 #else:
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105 tifffile.imsave( "WC_output_mask.tif", mask[:,:,:,0])
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106 tifffile.imsave("NU_output_mask.tif", mask[:,:,:,1])
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107 #end if
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108 #else:
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109 ## save single-channel mask outputs as a tiff
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110 tifffile.imsave("mask.tif", mask)
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111 #end if
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112 </configfile>
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113 </configfiles>
0
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114 <inputs>
2
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115 <param name="image" type="data" format="tiff, ome.tiff" label="Image containing the all marker(s)"/>
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116 <param name="nuclear_channels" type="text" value="0" label="The numerical indices of the channel(s) for the nuclear markers" help="No quotes, separated by comma. e.g. 0, 1."/>
0
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117 <param name="image_mpp" type="float" value="0.5" label="Resolution of the image in microns-per-pixel"/>
2
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118 <param name="squeeze" type="boolean" checked="true" label="Whether to np.squeeze the outputs before saving"/>
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119 <conditional name="compartment_select">
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120 <param name="compartment" type="select" label="Compartment for segmentation prediction: ">
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121 <option selected="true" value="whole-cell">Whole cell</option>
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122 <option value="nuclear">Nuclear</option>
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123 <option value="both">Both</option>
0
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124 </param>
2
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125 <when value="nuclear">
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126 <expand macro="nuclear_options_macro"/>
0
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127 </when>
2
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128 <when value="whole-cell">
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129 <expand macro="wc_options_macro"/>
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130 </when>
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131 <when value="both">
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132 <expand macro="nuclear_options_macro"/>
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133 <expand macro="wc_options_macro"/>
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134 </when>
0
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135 </conditional>
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136 </inputs>
2
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137
0
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138 <outputs>
2
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139 <data format="tiff" name="nu_mask" from_work_dir="NU_output_mask.tif" label="${tool.name} on ${on_string}: Nuclear Mask">
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140 <filter>compartment_select['compartment'] == 'both'</filter>
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141 </data>
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142 <data format="tiff" name="wc_mask" from_work_dir="WC_output_mask.tif" label="${tool.name} on ${on_string}: Whole Cell Mask">
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143 <filter>compartment_select['compartment'] == 'both'</filter>
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144 </data>
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145 <data format="tiff" name="mask" from_work_dir="mask.tif" label="${tool.name} on ${on_string}: Mask">
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146 <filter>compartment_select['compartment'] != 'both'</filter>
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147 </data>
0
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148 </outputs>
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149 <tests>
3
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150 <test expect_num_outputs="1">
2
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151 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
0
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152 <param name="compartment" value="nuclear" />
1
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153 <param name="image_mpp" value="0.65" />
2
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154 <param name="squeeze" value="True" />
0
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155 <output name="mask" ftype="tiff">
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156 <assert_contents>
2
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157 <has_size value="1049000" delta="1000" />
0
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158 </assert_contents>
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159 </output>
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160 </test>
3
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161 <test expect_num_outputs="1">
2
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162 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
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163 <param name="compartment" value="whole-cell" />
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164 <param name="wc_channels" value="0, 1" />
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165 <param name="maxima_threshold" value="0.075" />
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166 <param name="image_mpp" value="0.65" />
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167 <param name="squeeze" value="True" />
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168 <output name="mask" ftype="tiff">
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169 <assert_contents>
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170 <has_size value="1049000" delta="1000" />
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171 </assert_contents>
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172 </output>
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173 </test>
3
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174 <test expect_num_outputs="2">
2
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175 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
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176 <param name="compartment" value="both" />
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177 <param name="image_mpp" value="0.65" />
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178 <param name="wc_channels" value="1" />
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179 <param name="nuclear_channels" value="0" />
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180 <param name="squeeze" value="True" />
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181 <output name="wc_mask" ftype="tiff">
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182 <assert_contents>
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183 <has_size value="1049000" delta="1000" />
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184 </assert_contents>
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185 </output>
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186 <output name="nu_mask" ftype="tiff">
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187 <assert_contents>
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188 <has_size value="1049000" delta="1000" />
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189 </assert_contents>
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190 </output>
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191 </test>
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192 </tests>
0
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193 <help><![CDATA[
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194 ------
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195 Mesmer
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196 ------
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197
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198 **Mesmer** is a DeepCell application that uses a pre-trained model for predicting nuclear and whole cell segmentation from multiplex tissue data.
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199
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200 **Inputs**
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201 1. a multiple tissue image that includes a nuclear marker and optionally cytoplasm/membrane markers
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202
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203 **Outputs**
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204 1. the nuclear or whole cell segmentation mask
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205 ]]></help>
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206 <expand macro="citations" />
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207 </tool>