Project-MONAI / Project-MONAI/MONAILabel
Add Freeze option for deep grow
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- Dominant language
- Python
- Stars
- 891
- Forks
- 269
- Avg merge
- 15h 41m
- Merged PRs (30d)
- 1
Description
Is your feature request related to a problem? Please describe.
I am working on a segmentation problem or multiple organs. I have a segmentation model that does ok segmenting about 70% of the structures. I have trained another model deep grow to help me annotate. I start by running the segmentation model inference then use the load original labels.
Now I switch to deepgrow to so I can add on the missing areas say in the liver. when I click a couple of points then update, deepgrow clears all the liver labels and runs inference which is much worse than what I had.
Describe the solution you'd like
Please add a freeze option to deepgrow to append to the existing label. that is when clicked the new foreground label is added to what already exist in slicer.
I know that would not work when adding background points since you would want the model to remove extra overflowing labels. However, I never face this issue as it is simpler to remove extra labels by a brush
Describe alternatives you've considered
Tried to use deep grow. However, The model has low resolution and doesn't seem to be responsive with new clicks
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 tracing DeepGrow’s Slicer interaction from the existing-label loading and update/inference flow. Check how foreground and background clicks are handled, then verify the freeze behavior in Slicer: existing labels should be retained while new foreground output is appended, with background behavior considered separately.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- frontend, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100