Image segmentation

​The Allen Cell Structure 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.

It consists of two complementary elements:

1. Classic image segmentation workflows for 20 distinct intracellular structure localization patterns. A visual “lookup table” is outlining the modular algorithmic steps for each segmentation workflow. This provides an intuitive guide for selection or construction of new segmentation workflows for a user’s particular segmentation task. 
2. Human-in-the-loop iterative deep learning segmentation workflow trained on ground truth manually curated data from the images segmented with the segmentation workflow. Importantly, this module was not released yet.

Labkit is an open-source tool to segment truly large image data using sparse training data. It has an intuitive and responsive user interface based on Big Data Viewer, allowing users to conveniently browse and annotate even terabyte sized image volumes.

Update site: Labkit

SimpleITK provides a simplified interface to ITK in a variety of languages. A user can either download pre-built binaries, if they are available for the desired platform and language, or SimpleITK can be built from the source code. Currently, Python binaries are available on Microsoft Windows, GNU Linux and Mac OS X. C# and Java binaries are available for Windows. We are also working towards supporting R packaging.

ZEN and APEER – Open Ecosystem for integrated Machine-Learning Workflows

Open ecosystem for integrated machine-learning workflows to train and use machine-learning models for image processing and image analysis inside the ZEN software or on the APEER cloud-based platform

Highlights ZEN

• Simple User Interface for Labeling and Training
• Engineered Features Sets and Deep Feature Extraction + Random Forrest for Semantic Segmentation
• Object Classification workflows
• Probability Thresholds and Conditional Random Fields
• Import your own trained models as *.czann files (see: czmodel · PyPI)
• Import "AIModel Containes" from arivis AI for advanced Instance Segmentation
• Integration into ZEN Measurement Framework
• Support for Multi-dimensional Datasets and Tile Images
• open and standardized format to store trained models

ZEN Intellesis Segmentation

ZEN Intellesis - Pretrained Networks

Intellesis Object Classification

Highlights Aarivis AI

• Web-based tool to label datasets to train Deep Neural Networks
• Fully automated hyper-parameter tuning
• Export of trained models for semantic segmentation and AIModelContainer for Instance Segmentation

Annotation Tool