# empanada-napari
**The preprint describing this work is now available [on bioRxiv](https://www.biorxiv.org/content/10.1101/2022.03.17.484806v1).**
**Documentation for the plugin, including more detailed installation instructions, can be found [here](https://empanada.readthedocs.io/en/latest/empanada-napari.html).**
[empanada](https://github.com/volume-em/empanada) is a tool for deep learning-based panoptic segmentation of 2D and 3D electron microscopy images of cells.
This plugin allows the running of panoptic segmentation models trained in empanada within [napari](https://napari.org).
For help with this plugin please open an [issue](https://github.com/volume-em/empanada-napari/issues), for issues with napari specifically
raise an [issue here instead](https://github.com/napari/napari/issues).
## Implemented Models
- *MitoNet*: A generalist mitochondrial instance segmentation model.
## Example Datasets
Volume EM datasets for benchmarking mitochondrial instance segmentation are available from
[EMPIAR-10982](https://www.ebi.ac.uk/empiar/EMPIAR-10982/).
## Installation
It's recommended to have installed napari through [conda](https://docs.conda.io/en/latest/miniconda.html).
Then to install this plugin:
```shell
pip install empanada-napari
```
Launch napari:
```shell
napari
```
Look for empanada-napari under the "Plugins" menu.
## GPU Support
**Note: Mac doesn't support NVIDIA GPUS. This section only applies to Windows and Linux systems.**
As for any deep learning models, having a GPU installed on your system will significantly
increase model throughput (although we ship CPU optimized versions of all models with the plugin).
This plugin relies on torch for running models. If a GPU was found on your system, then you will see that the
"Use GPU" checkbox is checked by default in the "2D Inference" and "3D Inference" plugin widgets. Or if when running
inference you see a message that says "Using CPU" in the terminal that means a GPU is not being used.
Make sure that GPU drivers are correctly installed. In terminal or command prompt:
```shell
nvidia-smi
```
If this returns "command not found" then you need to [install the driver from NVIDIA](https://www.nvidia.com/download/index.aspx). Instead, if
if the driver is installed correctly, you may need to switch to the GPU enabled version of torch.
First, uninstall the current version of torch:
```shell
pip uninstall torch
```
Then [install torch >= 1.10 using conda for your system](https://pytorch.org/get-started/locally/).
This command should work:
```shell
conda install pytorch cudatoolkit=11.3 -c pytorch
```
## Citing this work
If you use results generated by this plugin in a publication, please cite:
```bibtex
@article {Conrad2022.03.17.484806,
author = {Conrad, Ryan and Narayan, Kedar},
title = {Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model},
elocation-id = {2022.03.17.484806},
year = {2022},
doi = {10.1101/2022.03.17.484806},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806},
eprint = {https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806.full.pdf},
journal = {bioRxiv}
}
```
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"description": "# empanada-napari\n\n**The preprint describing this work is now available [on bioRxiv](https://www.biorxiv.org/content/10.1101/2022.03.17.484806v1).**\n\n**Documentation for the plugin, including more detailed installation instructions, can be found [here](https://empanada.readthedocs.io/en/latest/empanada-napari.html).**\n\n[empanada](https://github.com/volume-em/empanada) is a tool for deep learning-based panoptic segmentation of 2D and 3D electron microscopy images of cells.\nThis plugin allows the running of panoptic segmentation models trained in empanada within [napari](https://napari.org).\nFor help with this plugin please open an [issue](https://github.com/volume-em/empanada-napari/issues), for issues with napari specifically\nraise an [issue here instead](https://github.com/napari/napari/issues).\n\n## Implemented Models\n\n - *MitoNet*: A generalist mitochondrial instance segmentation model.\n\n## Example Datasets\n\nVolume EM datasets for benchmarking mitochondrial instance segmentation are available from\n[EMPIAR-10982](https://www.ebi.ac.uk/empiar/EMPIAR-10982/).\n\n## Installation\n\nIt's recommended to have installed napari through [conda](https://docs.conda.io/en/latest/miniconda.html).\nThen to install this plugin:\n\n```shell\npip install empanada-napari\n```\n\nLaunch napari:\n\n```shell\nnapari\n```\n\nLook for empanada-napari under the \"Plugins\" menu.\n\n\n\n## GPU Support\n\n**Note: Mac doesn't support NVIDIA GPUS. This section only applies to Windows and Linux systems.**\n\nAs for any deep learning models, having a GPU installed on your system will significantly\nincrease model throughput (although we ship CPU optimized versions of all models with the plugin).\n\nThis plugin relies on torch for running models. If a GPU was found on your system, then you will see that the\n\"Use GPU\" checkbox is checked by default in the \"2D Inference\" and \"3D Inference\" plugin widgets. Or if when running\ninference you see a message that says \"Using CPU\" in the terminal that means a GPU is not being used.\n\nMake sure that GPU drivers are correctly installed. In terminal or command prompt:\n\n```shell\nnvidia-smi\n```\n\nIf this returns \"command not found\" then you need to [install the driver from NVIDIA](https://www.nvidia.com/download/index.aspx). Instead, if\nif the driver is installed correctly, you may need to switch to the GPU enabled version of torch.\n\nFirst, uninstall the current version of torch:\n\n```shell\npip uninstall torch\n```\n\nThen [install torch >= 1.10 using conda for your system](https://pytorch.org/get-started/locally/).\nThis command should work:\n\n```shell\nconda install pytorch cudatoolkit=11.3 -c pytorch\n```\n\n## Citing this work\n\nIf you use results generated by this plugin in a publication, please cite:\n\n```bibtex\n@article {Conrad2022.03.17.484806,\n\tauthor = {Conrad, Ryan and Narayan, Kedar},\n\ttitle = {Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model},\n\telocation-id = {2022.03.17.484806},\n\tyear = {2022},\n\tdoi = {10.1101/2022.03.17.484806},\n\tpublisher = {Cold Spring Harbor Laboratory},\n\tURL = {https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806},\n\teprint = {https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806.full.pdf},\n\tjournal = {bioRxiv}\n}\n```\n",
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