Purpose

Describe the problem you are trying to solve by packaging your tool as a napari plugin in 1-2 sentences. I should be able to read this alone and communicate the value to someone else.

Why does this matter to biologists?

The ilastik plugin allows Biologists to perform semantic segmentation on their images. Having this available in a common framework with other tools enables complex analysis pipelines and interactive exploration.

Why does this matter to you and your professional goals and responsibilities?

Since napari is expected to be a widely adopted Python-based image viewer, we would like to be a part of a broader ecosystem. Moreover, using napari could lower the maintenance efforts of maintaining an image viewer component for the ilastik development team.

Goals

What would you like to accomplish during the Alfa Cohort collaboration?

  1. Offer “bread and butter” classic Ilastik pixel-based classifier application as a napari plugin
  2. Evaluate napari’s opportunity to support other tools that the Ilastik group is developing

Non-goals

Are there any possible goals that are explicitly out of scope for the Alfa Cohort?

  1. Do not want to replace support for “classic” Ilastik

Scope

Key Flows

Show what the end-to-end experience will be for biologists.

A biologist will be able to

  1. Load data
  2. Select filters to compute and use as features
  3. Interactively label
  4. Evaluate results
  5. Predict everything
  6. Export
  7. Save project to be used in batch mode

Plan

Milestones

Status: To Do 📝, In Progress 🏗, In Review 🔎, Done ✅

Target Date Milestone Description Status
2021-02-24 Fixed Pipeline The plugin works with the predefined set of features. There is no interactive labelling yet: as a temporary solution, labels should be loaded from an external file. 📝
2021-03-17 Interaction Dataset can be labelled interactively. 📝
2021-04-07 Filters Filters are selectable. 📝
2021-04-28 Batch and Demo Images can be processed in a batch mode. Present the final plugin. 📝