{"name":"napari-bootstrapper","display_name":"Bootstrapper","visibility":"public","icon":"","categories":["Annotation","Segmentation","Dataset"],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-bootstrapper.make_cremi_sample_data","title":"cremi_c","python_name":"napari_bootstrapper:make_cremi_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.make_fluo_c2dl_huh7_sample_data","title":"fluo_c2dl_huh7","python_name":"napari_bootstrapper:make_fluo_c2dl_huh7_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.merge_labels","title":"Create Merge Labels","python_name":"napari_bootstrapper.post._merge_split_widget:merge_labels","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.delete_labels","title":"Create Delete Labels","python_name":"napari_bootstrapper.post._merge_split_widget:delete_labels","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.morph_labels","title":"Create Morph Labels","python_name":"napari_bootstrapper.post._merge_split_widget:morph_labels","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.split_labels","title":"Create Split Labels","python_name":"napari_bootstrapper.post._merge_split_widget:split_labels","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.filter_labels","title":"Create Filter Labels","python_name":"napari_bootstrapper.post._merge_split_widget:filter_labels","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-bootstrapper.Widget","title":"Bootstrapper","python_name":"napari_bootstrapper.widget:Widget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-bootstrapper.Widget","display_name":"Bootstrapper","autogenerate":false},{"command":"napari-bootstrapper.delete_labels","display_name":"Delete Labels","autogenerate":false},{"command":"napari-bootstrapper.merge_labels","display_name":"Merge Labels","autogenerate":false},{"command":"napari-bootstrapper.split_labels","display_name":"Split Labels","autogenerate":false},{"command":"napari-bootstrapper.morph_labels","display_name":"Morph Labels","autogenerate":false},{"command":"napari-bootstrapper.filter_labels","display_name":"Filter Labels","autogenerate":false}],"sample_data":[{"command":"napari-bootstrapper.make_cremi_sample_data","key":"napari-bootstrapper-cremi","display_name":"cremi_c"},{"command":"napari-bootstrapper.make_fluo_c2dl_huh7_sample_data","key":"napari-bootstrapper-fluo_c2dl_huh7","display_name":"fluo_c2dl_huh7"}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-bootstrapper","version":"0.2.0","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"A plugin to quickly generate dense ground truth with sparse labels","description":"# napari-bootstrapper\n\n[![License BSD-3](https://img.shields.io/pypi/l/napari-bootstrapper.svg?color=green)](https://github.com/ucsdmanorlab/napari-bootstrapper/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-bootstrapper.svg?color=green)](https://pypi.org/project/napari-bootstrapper)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-bootstrapper.svg?color=green)](https://python.org)\n[![tests](https://github.com/ucsdmanorlab/napari-bootstrapper/workflows/tests/badge.svg)](https://github.com/ucsdmanorlab/napari-bootstrapper/actions)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-bootstrapper)](https://napari-hub.org/plugins/napari-bootstrapper)\n\n- [Introduction](#introduction)\n- [Installation](#installation)\n- [Getting Started](#getting-started)\n- [Citation](#citation)\n- [Issues](#issues)\n- [Funding](#funding)\n\n## Introduction\n\n`napari-bootstrapper` is a tool to quickly generate dense 3D labels using sparse 2D labels within napari.\n\nDense 3D segmentations are generated using the 2D->3D method described in the preprint titled [_Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation_](https://www.biorxiv.org/content/10.1101/2024.06.14.599135v2). 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.\n\nThis 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_](https://github.com/ucsdmanorlab/bootstrapper) CLI tool.\n\n## Installation\n\nWe recommend installing `napari-bootstrapper` via conda and [pip]:\n\n1. Create a new environment called `napari-bootstrapper`:\n\n```bash\nconda create -n napari-bootstrapper -c conda-forge python==3.11 napari pyqt\n```\n\n2. Activate the newly-created environment:\n\n```\nconda activate napari-bootstrapper\n```\n\n3. You can install `napari-bootstrapper` via [pip]:\n\n```bash\npip install napari-bootstrapper\n```\n   - Or you can install the latest development version from github:\n\n```bash\npip install git+https://github.com/ucsdmanorlab/napari-bootstrapper.git\n```\n\n\n## Getting Started\nRun the following in your terminal:\n```bash\nconda activate napari-bootstrapper\nnapari\n```\n\n## Citation\n\nIf you find Bootstrapper useful in your research, please consider citing our **[preprint](https://www.biorxiv.org/content/10.1101/2024.06.14.599135v1)**:\n```\n@article {Thiyagarajan2024.06.14.599135,\n\tauthor = {Thiyagarajan, Vijay Venu and Sheridan, Arlo and Harris, Kristen M. and Manor, Uri},\n\ttitle = {Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation},\n\tyear = {2024},\n\tdoi = {10.1101/2024.06.14.599135},\n\tURL = {https://www.biorxiv.org/content/10.1101/2024.06.14.599135v2},\n}\n```\n\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://github.com/ucsdmanorlab/napari-bootstrapper/issues) along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[copier]: https://copier.readthedocs.io/en/stable/\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[napari-plugin-template]: https://github.com/napari/napari-plugin-template\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n\n\n## Funding\nChan-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).\n\nNSF NeuroNex Technology Hub Award (1707356), NSF NeuroNex2 Award (2014862)\n\n![image](https://github.com/ucsdmanorlab/bootstrapper/assets/64760651/4b4a6029-e1ba-42bb-ab8b-d9357cc46239)\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Vijay Venu Thiyagarajan","author_email":"vvenu@utexas.edu","maintainer":null,"maintainer_email":null,"license":"Copyright (c) 2025, Vijay Venu Thiyagarajan\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Science/Research","License :: OSI Approved :: BSD License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["numpy","scipy","scikit-image","torch","numba","gunpowder","magicgui","qtpy","pyqtgraph","matplotlib","napari","tqdm","lsds","mwatershed","tox; extra == \"testing\"","pytest; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","napari; extra == \"testing\"","pyqt5; extra == \"testing\""],"requires_python":">=3.11","requires_external":null,"project_url":null,"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}