Project-MONAI / Project-MONAI/MONAILabel
Support parallel annotations of multiple sequences
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- Dominant language
- Python
- Stars
- 891
- Forks
- 269
- Avg merge
- 15h 41m
- Merged PRs (30d)
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Description
Is your feature request related to a problem? Please describe.
We are working on a project where the interpretation of a pathological finding depends on the context of multiple MRI sequences. Specifically we need a T2 weighted fat saturated sequence and a T1 weighted sequence. Sometimes a third sequence might even be needed. Currently MONAILabel and Slicer do only support annotations of a single 3D volume.
Describe the solution you'd like
Annotation of multiple sequences, ideally in different windows that are synced with each other.
Describe alternatives you've considered
One can manually load images to ITK-SNAP. If multiple ITK-SNAP windows are opened, it will sync across the sequences. This is our current solution, but requires a lot of manual effort.
Additional context
This is our current workflow with an example segmentation.
The images are synced across the two ITK-SNAP windows, the segmentations are done manually.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by mapping MONAILabel's current single-3D-volume annotation workflow and its integration with Slicer. Compare the requested workflow with the existing ITK-SNAP multi-window approach described in the issue. Done means multiple MRI sequences can be annotated in synchronized views, including support for a possible third sequence.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100