ucsdmanorlab / napari-bootstrapper

A plugin to quickly generate ground truth with sparse labels

napari-bootstrapper

A plugin to quickly generate ground truth with sparse labels

---

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

You can install napari-bootstrapper in a conda environment by following these commands:

conda create -n napari python=3.10
conda activate napari
conda install pytorch pytorch-cuda=11.8 "numpy<=1.23.5" boost -c pytorch -c nvidia
    
pip install cython zarr matplotlib mahotas
pip install git+https://github.com/funkelab/funlib.geometry.git
pip install git+https://github.com/funkelab/funlib.persistence.git
pip install git+https://github.com/funkelab/daisy.git
pip install git+https://github.com/funkey/gunpowder.git
pip install git+https://github.com/funkelab/funlib.math.git
pip install git+https://github.com/funkelab/funlib.evaluate.git
pip install git+https://github.com/funkelab/funlib.learn.torch.git
pip install git+https://github.com/htem/waterz@hms
pip install git+https://github.com/funkelab/funlib.segment.git
pip install git+https://github.com/funkelab/lsd.git
pip install neuroglancer
pip install git+https://github.com/funkelab/funlib.show.neuroglancer.git
pip install tensorboard tensorboardx
pip install jsmin
pip install magic-class
pip install git+https://github.com/yajivunev/autoseg
pip install "napari[all]"
pip install git+https://github.com/salkmanorlab/napari-bootstrapper

Usage

1. Open napari with napari and load in your data.
2. Create a new Labels Layer napari.
   • Paint some labels on a single section (or a few) using napari's brush and fill tools.
   • Focus on critical areas of the image (ambiguous and obvious examples of boundaries and not boundaries).
3. Save Your Data as a zarr.
   • Open Plugins -> napari-bootstrapper. Open Bootstrapper.
   • Click on Save Data.
   • Set the filepath of the zarr to be create.
   • Set the voxel size (or resolution) in world units (nanometers/pixel)
4. Train model 1
   • Make sure voxel size is same as what you saved. If your saved data has voxel size 50, 8, 8, then set it here as 8, 8
   • Run.
5. Train model 2
   • Make sure voxel size is same as what you saved. If your saved data has voxel size 50,8,8, then set it here as 50, 8, 8
   • Run.
6. Get a coffee.
7. Run Inference.
   • Make sure all the dataset names, model checkpoint paths, and voxel size are all correct.
   • Run
8. Watershed
9. Segment
10. Update resulting segmentation using other widgets in iPlugins -> napari-bootstrapper. You can create a Points layer in napari and place points in 3D to:
    • Keep selected labels
    • Delete selected labels
    • Merge selected labels
    • Split selected label between two point
    • Morph selected labels
      • Dilation, Erosion
      • Morphological opening, closing
      • Remove small objects
11. Save Your Data. Use it to train again!
    • Refine, Rinse, Repeat
    • Make many 3D segmentations to feed to the same or a full 3D model.
    • You are now bootstrapping 3D ground truth segmentation.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-bootstrapper" is free and open source software

Issues

If you encounter any problems, please [file an issue] along with a detailed description.