How to use ROICaT

Listed below, we have a suite of easy to run notebooks for running the ROICaT pipelines.

First time users:

Try it out using our Google CoLab notebooks below which can be run fully remotely without installing anything on your computer.

Normal usage:

We recommend using our Jupyter notebooks which can be run locally on any computer.

TRACKING:

• Interactive notebook

• Google CoLab

CLASSIFICATION:

• Interactive notebook - Drawing

• Google CoLab - Drawing

• Interactive notebook - Labeling

• Interactive notebook - Train classifier

• Interactive notebook - Inference with classifier

OTHER:

• Custom data importing notebook

• Use the API to integrate ROICaT functions into your own code: Documentation.

• Run the full tracking pipeline using roicat.pipelines.pipeline_tracking with default parameters generated from roicat.util.get_default_paramaters().

General workflow:

• Pass ROIs through ROInet: Images of the ROIs are passed through a neural network which outputs a feature vector for each image describing what the ROI looks like.

• Classification: The feature vectors can then be used to classify ROIs:

  • A simple regression-like classifier can be trained using user-supplied labeled data (e.g. an array of images of ROIs and a corresponding array of labels for each ROI).

  • Alternatively, classification can be done by projecting the feature vectors into a lower-dimensional space using UMAP and then simply circling the region of space to classify the ROIs.

• Tracking: The feature vectors can be combined with information about the position of the ROIs to track the ROIs across imaging sessions/planes.