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Government of Canada – DTPR AI Register

AI-Assisted Yeast Cell Segmentation for Biomedical Research

Research & Development · Healthcare

What it collects

About a measurement
Anonymized data
  • Live-cell microscope images of yeast cultures used as the primary input. These are scientific imaging measurements of biological specimens with no personal information attached.

Government of Canada AI Register — YeastNet

Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & DevelopmentHealthcare

YeastNet is a research tool developed by the National Research Council Canada in collaboration with the University of Ottawa that automatically identifies and tracks individual yeast cells in microscope images. It is designed to help scientists extract more data from high-throughput biology experiments. The system processes microscope images of yeast cells only — no personal information is involved. It is currently a prototype and has been published as a research output.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Live-cell microscope images of yeast cultures used as the primary input. These are scientific imaging measurements of biological specimens with no personal information attached.

Government of Canada AI Register — YeastNet

Processing

Computer Vision
  • Applies image segmentation techniques to microscope imagery to detect, delineate, and track individual yeast cell boundaries across time-series frames.

Government of Canada AI Register — YeastNet

What it does

Sensing (Perceptive AI)
Human decides
  • Processes raw microscope image frames and converts them into structured cell detections — identifying cell boundaries and positions for downstream tracking analysis by researchers.

Government of Canada AI Register — YeastNet

Deciding (Analytical AI)
Human decides
  • Tracks individual yeast cells across image sequences, classifying and scoring cell identity over time to enable longitudinal biological analysis.

Government of Canada AI Register — YeastNet

Outputs

About a measurement
Anonymized data
  • Segmentation masks and cell-tracking outputs identifying the boundaries and trajectories of individual yeast cells across microscope image sequences. No personal data is produced.

Government of Canada AI Register — YeastNet

Run by

National Research Council Canada (NRC)
  • Federal government department responsible for developing and deploying YeastNet. The system was developed by the Government of Canada and is in development status.

Government of Canada AI Register — YeastNet

Built by

University of Ottawa
  • Academic collaborator that co-developed YeastNet alongside the National Research Council Canada.

Government of Canada AI Register — YeastNet

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Segmentation and tracking outputs are available to NRC Canada researchers and their academic collaborators at the University of Ottawa for use in biology experiments.
Not available to me
  • This system processes non-personal scientific data (microscope images of yeast cells). There is no personal data and therefore no individual access rights apply.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyYeastNet has been published as a prototype and its research findings are publicly available. However, the register notes that AI use is not disclosed to users at the point of interaction. Individuals wishing to understand how the system works may consult the published research or contact the National Research Council Canada.

Risks and safeguards

No risks or safeguards have been published for this system yet.