pcnaDeep-napari

A customized interface for single cell track visualisation based on pcnaDeep and napari.

👀 Under construction

You can get test image data from pcnaDeep demo data.

TODO: usage tutorials.

Requirements

pcnaDeep
napari>=0.4.12

Usage

• If you have the composite image:

  # image: composite image, PCNA fluorescence the first channel, bright field the last.
  # mask: PCNAdeep output binary objrct mask.
  # track: PCNAdeep output tracked object table.
  python launch.py --image data/MCF10A_demo_comp.tif --mask data/MCF10A_demo_mask.tif --track data/MCF10A_demo_tracks_refined.csv
  

• Otherwise, use raw uint16 images of the above two channels with automatic pre-processing steps.

  # sat: pixel saturation for rescaling PCNA and bright field.
  # gamma: gamma factor for processing PCNA.
  python launch.py --bf data/MCF10A_demo_bf.tif --pcna data/MCF10A_demo_pcna.tif --mask data/MCF10A_demo_mask.tif --track data/MCF10A_demo_tracks_refined.csv --sat 1 --gamma 1
  

This is not a napari plugin and you must launch the interface through the launch.py script.

Licence

pcnaDeep-napari is released under the Apache 2.0 license.