Empanada Napari

Panoptic segmentation algorithms for 2D and 3D electron microscopy in napari
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The preprint describing this work is now available on bioRxiv.

Documentation for the plugin, including more detailed installation instructions, can be found here.

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. For help with this plugin please open an issue, for issues with napari specifically raise an issue here instead.

Implemented Models

  • MitoNet: A generalist mitochondrial instance segmentation model.

Example Datasets

Volume EM datasets for benchmarking mitochondrial instance segmentation are available from EMPIAR-10982.


It's recommended to have installed napari through conda. Then to install this plugin:

pip install empanada-napari

Launch 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:


If this returns "command not found" then you need to install the driver from NVIDIA. 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:

pip uninstall torch

Then install torch >= 1.10 using conda for your system. This command should work:

conda install pytorch cudatoolkit=11.3 -c pytorch

Citing this work

If you use results generated by this plugin in a publication, please cite:

@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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Deep Learning
Instance Segmentation