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Segmenter

Allen Cell & Structure Segmenter
​
​tools for the 3D segmentation of intracellular structures
​The Allen Cell & Structure Segmenter (Segmenter) is a Python-based open source toolkit developed at the Allen Institute for Cell Science for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models.

​The toolkit takes advantage of the high replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes.
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Getting started with Segmenter
Graphic interface for Segmenter

​Learn how to use Segmenter via a GUI plugin for napari (details below)


Jupyter notebook

Look-up table demo ↗︎
A collection of workflow as a starting point
Segmenter code on GitHub

​Classic code ↗
Classic image segmentation workflow

ML code ↗
Iterative deep learning workflow

Model zoo ↗
Batch processing script and functions for running deep learning based segmentation models 
Resources 
  • Segmenter overview - video ↗​
  • Segmenter overview - written
  • BioRxiv paper ↗
  • Image.sc discussion forum ↗   
Get the Segmenter plugin
​Access the 3D segmentation power of Segmenter via an intuitive graphical user interface, newly available as the Allen Cell & Structure Segmenter plugin (Segmenter plugin) for the popular open source image analysis software napari. 

Version 1.0.0 (June 30, 2021)
  • All of the classic segmentation workflows in the look-up table are available in the plugin 
  • Adjust workflow parameters to maximize segmentation quality for your data
  • Save/load customized workflows
  • Run batch processing on default or customized workflows
Instruction
1. Install napari
If you don't have napari, follow napari's tutorial that walks you through various ways you can install the application. We recommend installing as a bundle app if you are a non-developer.
Open tutorial
2. Install the Segmenter plugin
Within the napari application, you can search for "napari-allencell-segmenter" plugin and install by accessing the menu item "Plugins" -> "Install/Uninstall Package(s)". View this video tutorial to install the plugin. 
See plugin details
Picture
Click on the image to view a video tutorial to install the plugin
Development notes
We continuously strive to make Segmenter more accessible to biologists and participated in the Napari Plugin Alfa Cohort to develop robust access to Segmenter’s core infrastructure and its classic segmentation workflows. The first release provides a user interface for all of the classic Segmenter workflows. In the future we plan to make the full functionality of Segmenter available in the plugin. 
Segmenter overview
Segmenter overview
Segmenter consists of two complementary elements, a classic image segmentation workflow, with a restricted set of algorithms and parameters, and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a “lookup table” reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward “human-in-the-loop” curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher.
​
To access the toolkit, please check out our Github repositories for the Machine Learning Code & the Classic Code, as well as the accompanying paper on bioRxiv.

​Note :
  1. Due to the lag between development and production cycles, the segmentation results on Data Download page are continuously being updated using our new toolkit. In the meantime, applying our ML code or Classic code on Github can produce our most recent segmentation results.
  2. We appreciate your feedback. Please visit the Segmenter section on image.sc discussion forum for community questions and comments. 
 

Video tutorials

To assist biologists and computational scientists with using Segmenter, take a look at our two video tutorials:
Allen Cell Structure Segmenter: Creating a new classic image segmentation workflow
Our Scientific Data Engineer, Ruian Yang, walks users through the steps to create a new classic segmentation workflow for 3D image processing. 
Allen Cell Structure Segmenter: Using Iterative Deep Learning
Our Scientist, Melissa Hendershott describes the steps to combine the classic segmentation steps with deep learning, and provides helpful tips throughout the demonstration.

Lookup table of classic image segmentation worksflows for 28 intracellular structure localization patterns

 Click on thumbnail to view video

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Full z-stack examples

*Note: all scale bars in these movies are 10μm

Sialyltransferase 1

Back to Lookup Table
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Fibrillarin

Back to Lookup Table
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Nucleophosmin

Back to Lookup Table
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Sec61 beta

Back to Lookup Table
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SERCA2

Back to Lookup Table
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LAMP-1

Back to Lookup Table
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TOM20

Back to Lookup Table
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Centrin-2

Back to Lookup Table
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Desmoplakin

Back to Lookup Table
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PMP34

Back to Lookup Table
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Connexin-43

Back to Lookup Table
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Rab-5A

Back to Lookup Table
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Beta catenin

Back to Lookup Table
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Tight junction protein ZO1

Back to Lookup Table
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Beta actin

Back to Lookup Table
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Non-muscle myosin IIB

Back to Lookup Table
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Alpha-actinin-1

Back to Lookup Table
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Alpha tubulin

Back to Lookup Table
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Troponin I, slow skeletal muscle

Back to Lookup Table
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Titin

Back to Lookup Table
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Lamin B1 (interphase-specific)

Back to Lookup Table
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Lamin B1 (mitosis-specific)

Back to Lookup Table
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H2B (interphase-specific)

Back to Lookup Table
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H2B (for mitosis, except for prometaphase/metaphase)

Back to Lookup Table
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H2B (prometaphase/metaphase)

Back to Lookup Table
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Nup153

Back to Lookup Table
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Paxillin

Back to Lookup Table
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SMC1A

Back to Lookup Table
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SON

Back to Lookup Table
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  • About
      Institute
      1. News feed
      2. What we do
      3. Publications
      4. Allen Institute | allenInstitute.org
      5. Careers
      Site
      1. Home page
      2. Site updates
      3. Archived content
  • Allen Cell Collection
      Order cells & plasmids
      1. Cell Catalog
      2. Cell Catalog quickview
      3. Cell Shorts (documentaries on labs using our cells)
      4. Support forum
      Lab methods
      1. Instructional videos for success in the lab
      2. Standard operating procedures (written methods)
      3. Illustrated overviews
      About our hiPS cells
      1. hiPS Cell Structure Overview
      2. Visual Guide to Human Cells
      3. Cell structure observations
      4. Why endogenous tagging?
      5. Differentiation into cardiomyocytes
      6. Genomics
      7. Download cell data (images, genomics, features)
  • Data & Digital Tools
      Online image analysis
      1. Cell Feature Explorer (plotting & 3D viewer)
      2. 3D cell viewer (pre2018)
      3. Deep cell zoom (216,016 cells)
      Online modeling viewers
      1. Visual Guide to Human Cells
      2. Simularium (4D visual analysis)
      3. Integrated Mitotic Stem Cell
      4. └ Z-stack viewer
      5. └ 3D viewer
      6. Allen Integrated Cell viewer
      7. Label-free examples viewer
      8. 3D probabilistic model viewer
      Desktop tools
      1. Allen Cell & Structure Segmenter
      2. AGAVE 3D pathtrace image viewer
      Data & code
      1. Download cell data (images, genomics, features)
      2. Code repositories & software
  • Analysis & Modeling
      Allen Integrated Cell models
      1. Overview
      2. Integrated Mitotic Stem Cell
      3. └ Z-stack viewer
      4. └ 3D viewer
      5. Label-free Determination
      6. └ 3D viewer
      7. 3D Probabilistic Modeling
      8. └ 3D viewer
      9. Visual Guide to Human Cells
      4D biology models
      1. Simularium (online 4D viewer)
      Methodologies
      1. Drug perturbation pilot study
      2. hiPS cells during mitosis
      3. Differentiation into cardiomyocytes
  • Publications
      Articles
      1. All journal publications
      2. Preprints (biorxiv, arxiv)
      Posters
      1. Select posters
  • Education
      Education resources
      1. All Resources
      2. Teaching materials
      Online tools popular with teachers
      1. Visual Guide to Human Cells
      2. Integrated Mitotic Stem Cell
      3. Cell Feature Explorer (interactive plotting & 3D viewer)
      4. 3D cell viewer (pre2018 data)
      5. hiPS cell structure overview
  • Support
      Questions
      1. FAQs
      2. Forum
      Tutorials for digital tools
      1. Digital tool tutorials with videos
      2. Visual Guide tutorial
      3. AGAVE user guide
      Lab methods
      1. Instructional videos for success in the lab
      2. Standard operating procedures (written methods)
      3. Illustrated overviews
  • 🔍
      SEARCHBAR