Bootstrapper
A plugin to quickly generate dense ground truth with sparse labels
Introduction¶
napari-bootstrapper is a tool to quickly generate dense 3D labels using sparse 2D labels within napari.
Dense 3D segmentations are generated using the 2D->3D method described in the preprint titled Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation. In the preprint, we show sparse 2D annotations made in ~10 minutes on a single section can generate dense 3D segmentations that are reasonably good starting points for refining or bootstrapping.
This plugin is limited to the 2D->3D method and is intended for small volumes that can fit in memory. For more complex bootstrapping workflows, dedicated 3D models, and block-wise processing of large volumes, we recommend using the Bootstrapper CLI tool.
Installation¶
We recommend installing napari-bootstrapper via conda and pip:
- Create a new environment called
napari-bootstrapper:
conda create -n napari-bootstrapper -c conda-forge python==3.11 napari pyqt- Activate the newly-created environment:
conda activate napari-bootstrapper- You can install
napari-bootstrappervia pip:
pip install napari-bootstrapper- Or you can install the latest development version from github:
pip install git+https://github.com/ucsdmanorlab/napari-bootstrapper.gitGetting Started¶
Run the following in your terminal:
conda activate napari-bootstrapper
napariCitation¶
If you find Bootstrapper useful in your research, please consider citing our preprint:
@article {Thiyagarajan2024.06.14.599135,
author = {Thiyagarajan, Vijay Venu and Sheridan, Arlo and Harris, Kristen M. and Manor, Uri},
title = {Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation},
year = {2024},
doi = {10.1101/2024.06.14.599135},
URL = {https://www.biorxiv.org/content/10.1101/2024.06.14.599135v2},
}Issues¶
If you encounter any problems, please file an issue along with a detailed description.
Funding¶
Chan-Zuckerberg Imaging Scientist Award DOI https://doi.org/10.37921/694870itnyzk from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (funder DOI 10.13039/100014989).
NSF NeuroNex Technology Hub Award (1707356), NSF NeuroNex2 Award (2014862)
Version:
- 0.1.1
Last updated:
- 23 May 2025
First released:
- 23 May 2025
License:
- Copyright (c) 2025, Vijay Venu Thiyagarajan All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Supported data:
- Information not submitted
Plugin type:
GitHub activity:
- Stars: 0
- Forks: 0
- Issues + PRs: 0