This course shows how to create or wrap an image processing or ML workflow so that it can be used by the HistomicsTK / Digital Slide Archive platform, generating editable annotations that can be accessed and used in other tools.


  • Learn how to start and manage a local instance of the HistomicsTK platform
  • Learn how annotations and metadata are created and accessed
  • Learn how to wrap an image progressing algorithm to run on one or a batch of histology images
  • Learn how ML algorithms can be used


  • Basic Python programming knowledge
  • Basic understanding of data processing
  • Basic knowledge of ML or some minor knowledge of image processing
  • A linux-based computer with docker and docker compose installed on it (optional -- we can provide some web-based computing resources during the course)


This course is approximately 8 hours, which can be divided into 2 sessions.

Introduction to HistomicsTK

  • History
  • Open Source License
  • Platform support
  • Use-cases


  • Default docker containers
  • Girder for data management
  • Slicer CLI execution model
  • Task dockers

Managing Data

  • Upload / Import files
  • Manage permissions
  • Annotations (lots of variations)
  • Export / Download
  • The API (and how to use it from a Jupyter notebook or the command line)
  • Running Existing Algorithms

Wrapping a simple image processing task

  • A tunable algorithm for foreground / background discrimination for H&E images
  • Turn your algorithm into a runnable docker image
  • Tile aggregation
  • Running the generated docker image on the system

As time permits:

Wrapping a simple ML tensorflow task

  • Making an example ML task into a command-line program
  • Wrapping it as a docker image

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