Model-based segmentation

ToDo: add a few more but not too many specialised concepts under here.

Description

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

APEER

Description

This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.

Stardist
Description

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 + RF Forrest for Segmentation
  • Object Classification workflows
  • Probability Thresholds and Conditional Random Fields
  • Import your own trained models with support for TensorFlow2 and ONNX
  • 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 Segmentation - Training UI

ZEN Intellesis - Pretrained Networks

ZEN Intellesis Segmentation - Use Deep Neural Networks

Intellesis Object Classification

ZEN Object Classification

Highlights APEER

  • Web-based tool to label datasets to train Deep Neural Networks
  • Fully automated hyper-parameter tuning
  • Export of trained models 
APEER Annotation Tool

APEER Annotation Tool