Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Scripts for structure segmentation.

Project description

## Overview

The Allen Cell Structure Segmenter is a Python-based open source toolkit developed for 3D segmentation of intracellular structures in fluorescence microscope images, developed at the Allen Institute for Cell Science. This toolkit 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. The Allen Cell Structure Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher. More details including algorithms, validations, and examples can be found in our [bioRxiv paper](https://www.biorxiv.org/content/10.1101/491035v1) or [allencell.org/segmenter](allencell.org/segmenter).

Note: This repository only has the code for the “Classic Image Segmentation Workflow”. The deep learning part can be found at [https://github.com/AllenInstitute/aics-ml-segmentation](https://github.com/AllenInstitute/aics-ml-segmentation)

## Installation

Our package is implemented in Python 3.6. We suggest managing Python packages using conda. For detailed instructions for installation on different operating systems, see the pages below.

[Installation on Linux](./docs/installation_linux.md) (Ubuntu 16.04.5 LTS is the OS we used for development)

[Installation on MacOS](./docs/installation_mac.md)

[Installation on Windows](./docs/installation_windows.md)

## Use the package

The Allen Cell Structure Segmenter is essentially a collection of an array of image analysis and visualization algorithms implemented in Python 3, including [ITK](https://itkpythonpackage.readthedocs.io/en/latest/), [scikit-image](http://scikit-image.org/docs/stable/), and also some our new algorithms. Thus, by installing the Allen Cell Structure Segmenter, you already have many Python “weapons” for image analysis and visualization on your machine. :hammer: :wrench:

Our package is designed (1) to provide a simple tool for cell biologists to quickly obtain intracellular structure segmentation with reasonable accuracy and robustness over a large set of images, and (2) to facilitate advanced development and implementation of more sophisticated algorithms in a unified environment by more experienced programmers.

Visualization is a key component in algorithm development and validation of results (qualitatively). Right now, our toolkit utilizes [itk-jupyter-widgets](https://github.com/InsightSoftwareConsortium/itk-jupyter-widgets), which is a very powerful visualization tool, primarily for medical data, which can be used in-line in Jupyter notebooks. Some cool demo videos can be found [here](https://www.youtube.com/playlist?list=PL2lHcsoU0YJsh6f8j2vbhg2eEpUnKEWcl).

### Part 1: Quick Start

After following the installation instructions above, users will find that the classic image segmentation workflow in the toolkit is:

  1. formulated as a simple 3-step workflow for solving 3D intracellular structure segmentation problem using restricted number of selectable algorithms and tunable parameters
  2. accompanied by a [“lookup table”](./docs/toolkit_paper_lookup_table_20181206.pdf) with 20 representative structure localization patterns and their results as a reference, as well as the Jupyter notebook for these workflows as a starting point. The [pseudocode of all 20 workflows](./docs/toolkit_paper_SI_vFinalForBioRxiv.pdf) are also provided.

Typically, we use Jupyter notebook as a “playground” to explore different algorithms and adjust the parameters. After determining the algorithms and parameters, we use Python scritps to do batch processing/validation on a large number of data.

You can find a [DEMO on a real example](https://github.com/AllenInstitute/aics-ml-segmentation/blob/master/docs/demo_1.md) on our tutorial page

### Part 2: API

The list of high-level wrappers/functions used in the package can be found [HERE](./docs/API.md). We are working on additional documentations and examples for advanced users/developers.

## Level of Support We are offering it to the community AS IS; we have used the toolkit within our organization. We are not able to provide guarantees of support. However, we welcome feedback and submission of issues. Users are encouraged to sign up on our [Allen Cell Discussion Forum](https://forum.allencell.org/) for community quesitons and comments.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for aicssegmentation, version 0.1.15
Filename, size File type Python version Upload date Hashes
Filename, size aicssegmentation-0.1.15-py2.py3-none-any.whl (57.7 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page