segmentation

Boonelab segmentation for microscopy images

https://github.com/BooneAndrewsLab/segmentation

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
    1 of 5 committers (20.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
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    Low similarity (7.6%) to scientific vocabulary

Keywords

biology cells microscopy morphology segmentation
Last synced: 6 months ago · JSON representation

Repository

Boonelab segmentation for microscopy images

Basic Info
  • Host: GitHub
  • Owner: BooneAndrewsLab
  • License: mit
  • Language: C++
  • Default Branch: master
  • Homepage:
  • Size: 48.8 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 3
  • Open Issues: 1
  • Releases: 0
Topics
biology cells microscopy morphology segmentation
Created almost 8 years ago · Last pushed almost 5 years ago
Metadata Files
Readme License

README.md

Python package Python package

Fast Mixture Model Segmentation

Fast mixture model segmentation used in Boone and Andrews labs

Python requirements

  • Numpy
  • Cython
  • scikit-image

Installation

Create a virtual environment (optional) sh $ virtualenv -ppython3 segmentation-env $ source segmentation-env/bin/activate

Install python requirements (needed to build the package)

sh $ pip install numpy cython

Install our library (pulls in all other dependencies)

sh $ pip install segmentation

Usage example

Programmatically:

```python from segmentation import segmentation from segmentation import watershed from skimage.io import imread

image = imread('./image.tiff', plugin='tifffile')[1] # Read channel 1 of a tiff/flex im = segmentation.blurframe(image) # gaussian blur segmented, _ = segmentation.mixturemodel(im, debug=True) # second return argument is currently unused labels = watershed(im, segmented) ```

Command line

```sh $ segment -h # for usage information

$ segment -o segmented.data image.tiff ```

Output is a memmaped labels array. You can read it like this: python from numpy import memmap labels = memmap('segmented.data', dtype='int32', shape=(1010, 1346)) # shape is same as input image

Owner

  • Name: Boone-Andrews Lab
  • Login: BooneAndrewsLab
  • Kind: organization
  • Location: Toronto, Canada

Donnelly Centre, University of Toronto

GitHub Events

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Last synced: about 2 years ago

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  • Total Commits: 30
  • Total Committers: 5
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.367
Past Year
  • Commits: 0
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  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
usajusaj u****j@u****g 19
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Matej Usaj u****j 4
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Matej Usaj m****j@u****a 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 8 months ago

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  • Total issues: 2
  • Total pull requests: 2
  • Average time to close issues: 1 day
  • Average time to close pull requests: 1 minute
  • Total issue authors: 2
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  • Average comments per issue: 0.5
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  • Bot issues: 0
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Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
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  • Issue authors: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
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Top Authors
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  • leesin9527 (1)
  • pdavis39 (1)
Pull Request Authors
  • usajusaj (2)
Top Labels
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Dependencies

environment.yml conda
  • cython
  • gcc_linux-64
  • numpy
  • scikit-image
requirements.txt pypi
  • cython *
  • numpy *
  • scikit-image *
  • scipy *
setup.py pypi
  • numpy *
  • scikit-image *
  • scipy *