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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 130ef7e8ae6cab35866b72617e53d35acc11475a
author goeckslab
date Wed, 28 Feb 2024 20:52:00 +0000
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2675ff99a53b planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 130ef7e8ae6cab35866b72617e53d35acc11475a
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1 <tool id="mesmer" name="Perform segmentation of multiplexed tissue data" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
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2 <description>with Mesmer</description>
0
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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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7
4
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8 <edam_operations>
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9 <edam_operation>operation_3443</edam_operation>
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10 </edam_operations>
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11
0
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12 <expand macro="requirements"/>
1
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13 <expand macro="stdio"/>
0
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15 <command detect_errors="exit_code"><![CDATA[
2
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16 python $script
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17 ]]></command>
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18 <configfiles>
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19 <configfile name="script">
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20 import argparse
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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21 import os
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22 import sys
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24 import numpy as np
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25 import tifffile
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26 import zarr
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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28 from deepcell.applications import Mesmer
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30 level = 0
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31 is_ome=False
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32 #if $image.file_ext == "ome.tiff":
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33 is_ome=True
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34 #end if
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36 ## Grab params
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37 nuc_kwargs = {}
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38 wc_kwargs = {}
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41 with tifffile.TiffFile("$image", is_ome=is_ome) as tiff:
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43 # Read single pyramid level
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44 level_array = zarr.open(tiff.aszarr(series = 0, level = level))
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46 ## grab the nuclear and membrane channels based on their indices add the markers along the channel axis
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47 ## Tifffile should always read as (channel,X,Y)
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48 #if $compartment_select.compartment != 'whole-cell':
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49 nuc_kwargs = {
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50 #if $compartment_select.nuclear_options.pixel_expansion != '':
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51 'pixel_expansion': $compartment_select.nuclear_options.pixel_expansion,
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52 #end if
3
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53 #for $key, $value in $compartment_select.nuclear_options.adv_options.items():
2
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54 '$key': $value,
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55 #end for
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56 'maxima_threshold': $compartment_select.nuclear_options.maxima_threshold
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57 }
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58 #end if
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59
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60 nuclear_indices = [int(x) for x in $nuclear_channels.split(',')]
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61 nuclear_channels = level_array.oindex[nuclear_indices, :, :]
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62 nuclear_channels = np.sum(nuclear_channels, axis = 0)
0
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63
2
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64 #if $compartment_select.compartment != 'nuclear':
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65 wc_kwargs = {
3
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66 #for $key, $value in $compartment_select.wc_options.adv_options.items():
2
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67 '$key': $value,
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68 #end for
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69 'maxima_threshold': $compartment_select.wc_options.maxima_threshold
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70 }
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71
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72 membrane_indices = [int(x) for x in $compartment_select.wc_options.wc_channels.split(',')]
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73 membrane_channels = level_array.oindex[membrane_indices, :, :]
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74 membrane_channels = np.sum(membrane_channels, axis = 0)
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75 #end if
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76
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77 ## stack the nuclear and membrane composite channels with nuclear in channel 0 and mem in channel 1
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78 ## mesmer expects dimensions to be (X,Y,Channel) so axis = -1
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79 #if $compartment_select.compartment == 'nuclear':
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80 membrane_channels = np.zeros(nuclear_channels.shape)
0
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81 #end if
2
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82 formatted_image = np.stack((nuclear_channels,membrane_channels), axis=-1)
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83
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84 ## add batch dimension. Will have to be squeezed out later
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85 formatted_image = np.expand_dims(formatted_image, 0)
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86
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87 ## Create the application
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88 app = Mesmer()
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89
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90 ## Run segmentation
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91 mask = app.predict(
187918c47051 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mesmer commit 40737a3341bb2352f4f8560889bb53362fd624be
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92 formatted_image,
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93 image_mpp = $image_mpp,
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94 compartment = "$compartment_select.compartment",
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95 pad_mode = 'constant',
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96 postprocess_kwargs_whole_cell = wc_kwargs,
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97 postprocess_kwargs_nuclear = nuc_kwargs)
0
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98
2
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99 #if $squeeze:
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100 mask = np.squeeze(mask)
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101 #end if
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102
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103 #if $compartment_select.compartment == 'both':
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104 ## split the two-channel mask into separate outputs
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105 #if $squeeze:
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106 tifffile.imsave( "WC_output_mask.tif", mask[:,:,0])
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107 tifffile.imsave("NU_output_mask.tif", mask[:,:,1])
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108 #else:
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109 tifffile.imsave( "WC_output_mask.tif", mask[:,:,:,0])
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110 tifffile.imsave("NU_output_mask.tif", mask[:,:,:,1])
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111 #end if
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112 #else:
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113 ## save single-channel mask outputs as a tiff
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114 tifffile.imsave("mask.tif", mask)
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115 #end if
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116 </configfile>
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117 </configfiles>
0
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118 <inputs>
2
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119 <param name="image" type="data" format="tiff, ome.tiff" label="Image containing the all marker(s)"/>
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120 <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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121 <param name="image_mpp" type="float" value="0.5" label="Resolution of the image in microns-per-pixel"/>
2
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122 <param name="squeeze" type="boolean" checked="true" label="Whether to np.squeeze the outputs before saving"/>
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123 <conditional name="compartment_select">
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124 <param name="compartment" type="select" label="Compartment for segmentation prediction: ">
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125 <option selected="true" value="whole-cell">Whole cell</option>
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126 <option value="nuclear">Nuclear</option>
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127 <option value="both">Both</option>
0
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128 </param>
2
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129 <when value="nuclear">
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130 <expand macro="nuclear_options_macro"/>
0
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131 </when>
2
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132 <when value="whole-cell">
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133 <expand macro="wc_options_macro"/>
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134 </when>
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135 <when value="both">
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136 <expand macro="nuclear_options_macro"/>
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137 <expand macro="wc_options_macro"/>
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138 </when>
0
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139 </conditional>
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140 </inputs>
2
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141
0
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142 <outputs>
2
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143 <data format="tiff" name="nu_mask" from_work_dir="NU_output_mask.tif" label="${tool.name} on ${on_string}: Nuclear Mask">
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144 <filter>compartment_select['compartment'] == 'both'</filter>
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145 </data>
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146 <data format="tiff" name="wc_mask" from_work_dir="WC_output_mask.tif" label="${tool.name} on ${on_string}: Whole Cell Mask">
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147 <filter>compartment_select['compartment'] == 'both'</filter>
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148 </data>
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149 <data format="tiff" name="mask" from_work_dir="mask.tif" label="${tool.name} on ${on_string}: Mask">
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150 <filter>compartment_select['compartment'] != 'both'</filter>
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151 </data>
0
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152 </outputs>
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153 <tests>
3
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154 <test expect_num_outputs="1">
2
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155 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
0
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156 <param name="compartment" value="nuclear" />
1
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157 <param name="image_mpp" value="0.65" />
2
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158 <param name="squeeze" value="True" />
0
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159 <output name="mask" ftype="tiff">
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160 <assert_contents>
2
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161 <has_size value="1049000" delta="1000" />
0
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162 </assert_contents>
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163 </output>
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164 </test>
3
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165 <test expect_num_outputs="1">
2
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166 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
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167 <param name="compartment" value="whole-cell" />
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168 <param name="wc_channels" value="0, 1" />
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169 <param name="maxima_threshold" value="0.075" />
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170 <param name="image_mpp" value="0.65" />
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171 <param name="squeeze" value="True" />
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172 <output name="mask" ftype="tiff">
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173 <assert_contents>
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174 <has_size value="1049000" delta="1000" />
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175 </assert_contents>
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176 </output>
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177 </test>
3
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178 <test expect_num_outputs="2">
2
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179 <param name="image" value="deepcell_test.tiff" ftype="tiff"/>
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180 <param name="compartment" value="both" />
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181 <param name="image_mpp" value="0.65" />
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182 <param name="wc_channels" value="1" />
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183 <param name="nuclear_channels" value="0" />
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184 <param name="squeeze" value="True" />
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185 <output name="wc_mask" ftype="tiff">
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186 <assert_contents>
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187 <has_size value="1049000" delta="1000" />
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188 </assert_contents>
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189 </output>
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190 <output name="nu_mask" ftype="tiff">
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191 <assert_contents>
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192 <has_size value="1049000" delta="1000" />
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193 </assert_contents>
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194 </output>
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195 </test>
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196 </tests>
0
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197 <help><![CDATA[
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198 ------
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199 Mesmer
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200 ------
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201
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202 **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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203
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204 **Inputs**
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205 1. a multiple tissue image that includes a nuclear marker and optionally cytoplasm/membrane markers
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206
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207 **Outputs**
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208 1. the nuclear or whole cell segmentation mask
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209 ]]></help>
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210 <expand macro="citations" />
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211 </tool>