name: "ilastik_multicut"
description: Ilastik is a tool that utilizes machine learning algorithms to classify pixels, segment, track and count cells in images. Ilastik contains a graphical user interface to interactively label pixels. However, this nextflow module will implement the --headless mode, to apply pixel classification using a pre-trained .ilp file on an input image.
keywords:
  - multicut
  - segmentation
  - pixel classification
tools:
  - "ilastik":
      description: "Ilastik is a user friendly tool that enables pixel classification, segmentation and analysis."
      homepage: "https://www.ilastik.org/"
      documentation: "https://www.ilastik.org/documentation/"
      tool_dev_url: "https://github.com/ilastik/ilastik"
      licence: "GPL3"
input:
  - meta:
      type: map
      description: |
        Groovy Map containing sample information
        e.g. [ id:'test', single_end:false ]
  - h5:
      type: file
      description: h5 file containing image stack to classify file
      pattern: "*.{h5,hdf5}"
  - meta2:
      type: map
      description: |
        Groovy Map containing sample information
        e.g. [ id:'test', single_end:false ]
  - ilp:
      type: file
      description: Trained ilastik .ilp project file
      pattern: "*.{ilp}"
  - meta3:
      type: map
      description: |
        Groovy Map containing sample information
        e.g. [ id:'test', single_end:false ]
  - probs:
      type: file
      description: Probability map for boundary based segmentation
      pattern: "*.{h5,,hdf5}"
output:
  - meta:
      type: map
      description: |
        Groovy Map containing sample information
        e.g. [ id:'test', single_end:false ]
  - versions:
      type: file
      description: File containing software versions
      pattern: "versions.yml"
  - out_tiff:
      type: file
      description: Multicut segmentation mask output.
      pattern: "*.{tiff}"
authors:
  - "@FloWuenne"
maintainers:
  - "@FloWuenne"
