Machine learning

This module is for applying classification models on objects. It downloads from Cytomine-Core annotation images and coordinate of annotated objects from project(s) and build a annotation classification model which is saved locally. It downloads from Cytomine-Core annotations images from an image (e.g. detected by an object finder), apply a classification model (previously saved locally), and uploads to Cytomine-Core annotation terms (in a userjob layer).

This module is for learning classification models from ground-truth data (supervised learning). It downloads from Cytomine-Core annotation images and coordinate of annotated objects from project(s) and build a annotation classification model which is saved locally.  

It is used by Cytomine DataMining applications: classification_validation, classification_model_builder, classification_prediction, segmentation_model_builder and segmentation_prediction. But it can be run without Cytomine on local data (using dir_ls and dir_ts arguments).

SLDC is an open-source Python workflow. SLDC stands for Segment Locate Dispatch Classify. This framework aims at facilitating the development of algorithms for detecting objects in multi-gigapixel images. Particularly, it provides algorithm developers with a structure to define problem-dependent components of their processing workflow (i.e. segmentation and classification) in a concise way. Every other concern such as parallelization and large image handling are encapsulated by the framework. It also features a powerful and customizable logging system and some components to apply several workflows one after another on a same image. SLDC can work on local images or interact with Cytomine

Example image:

Toy image data

The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in Chanouard et al (2014) as "method 9" and is described in detail in its supplementary PDF (page 65).

Dependencies

Following plugins are required.

1. JAR to be placed under IJ plugin directory
2. A pdf file with instructions and output description is also available in the zip .
3. MTrackJ : Used for visualization of tracks. Preinstalled in Fiji.
4. Imagescience.jar: This library is used by MTrackJ. Use update site to install this plugin.
5. jama.jar. Preinstalled in Fiji.

##Advantages:

• support blinking (with a parameters allowing it or not)
• fast,
• can be used in batch, some analysis results provided.
• No dynamic model.
• The tracking part is not dependent of ImageJ.

Pitfalls:

• does not support division
• the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs.
• No Dynamic model (so less good results but can be used for a first study of the kind of movements)

##The sample data

The parameters used for this example data Beads, were

1. detection: 150
2. the max distance in pixels: 20
3. max allowed disappearance in frame: 1

An example macro introduced in the documentation page of the ImageJ plugin Trainable Weka Segmentation (in Fiji, it's bundled). A segmentation protocol based on machine learning. Full macro is available in the "Download" Link. 

This plugin can be trained to learn from the user input and perform later the same task in unknown (test) data. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka. Segmentation: it provides a labeled result based on the training of a chosen classifier. Trainable Weka Segmentation Complete macro example is at the end of the page.