Add mccellpose as new segmenter

mccellpose is a custom mcmicro-oriented wrapper around the Cellpose 4 Python
API. It uses constant memory and low VRAM compared to the standard cellpose
command line script by iterating over the image in overlapping tiles rather
than processing the whole thing at once. Command line options are different
from the standard script and designed for the mcmicro use case.

Author: jmuhlich

config/defaults.yml

@@ -110,6 +110,16 @@ modules:
       model: --pretrained_model
       idxbase: 0
       watershed: 'no'
+    -
+      name: mccellpose
+      container: labsyspharm/mccellpose
+      version: 1.0.3
+      cmd: mccellpose --use-gpu --jobs 2 --expand-size 2 --channel 1 --output-cell cell.ome.tif --output-nucleus nucleus.ome.tif
+      input: --input
+      channel: --channel
+      model: --pretrained_model
+      idxbase: 1
+      watershed: 'no'
   watershed:
     name: s3seg
     container: labsyspharm/s3segmenter

lib/mcmicro/Opts.groovy

@@ -219,11 +219,23 @@ static def parseParams(gp, fns, fnw) {
         mcp.modules['staging'] = mcp.modules['staging'][0]
 
     // Filter segmentation modules based on --segmentation
+    if (mcp.workflow.segmentation instanceof String) {
+        if (mcp.workflow.segmentation.contains(',')) {
+            throw new Exception('Use an explicit list with square brackets to specify multiple segmenter modules, e.g. [mod1, mod2]')
+        }
+        mcp.workflow.segmentation = [mcp.workflow.segmentation]
+    }
     mcp.modules['segmentation'] = mcp.modules['segmentation'].findAll{
         mcp.workflow.segmentation.contains(it.name)
     }
 
     // Filter downstream modules based on --downstream
+    if (mcp.workflow.downstream instanceof String) {
+        if (mcp.workflow.downstream.contains(',')) {
+            throw new Exception('Use an explicit list with square brackets to specify multiple downstream modules, e.g. [mod1, mod2]')
+        }
+        mcp.workflow.downstream = [mcp.workflow.downstream]
+    }
     mcp.modules['downstream'] = mcp.modules['downstream'].findAll{
         mcp.workflow.downstream.contains(it.name)
     }

main.nf

@@ -147,6 +147,7 @@ workflow segmentation {
     // Merge against instance segmentation outputs
     instSeg = allpmaps.filter{ _1, _2, _3, ws -> ws == 'no' }
         .map{ mtd, tag, _3, _4 -> tuple("${mtd}-${tag}", _3) }.groupTuple()
+        .map{ mt, paths -> tuple(mt, paths.flatten()) }
 
   emit:
     s3seg.out.segmasks.mix(instSeg)