# napari-aiSEGcell
[](https://github.com/CSDGroup/napari-aisegcell/raw/main/LICENSE)
[](https://pypi.org/project/napari-aisegcell)
[](https://python.org)
[](https://github.com/CSDGroup/napari-aisegcell/actions)
[](https://codecov.io/gh/CSDGroup/napari-aisegcell)
[](https://napari-hub.org/plugins/napari-aisegcell)
A [napari] plugin to segment cell nuclei and whole cells in bright field microscopy images. `napari-aisegcell` uses
[aisegcell] for segmentation. This plugin can only be used to use already trained models for segmentation. If you
want to train a new model use [aisegcell]. Please cite [this paper](#citation) if you are using this plugin in your
research.
----------------------------------
This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
<!--
Don't miss the full getting started guide to set up your new package:
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## Contents
- [Installation](#installation)
- [Command line](#command-line)
- [One-click](#one-click)
- [Data](#data)
- [Documentation](#documentation)
- [First launch](#first-launch)
- [Launch napari]((#launch-napari))
- [Layer mode](#layer-mode)
- [Batch mode](#batch-mode)
- [Trained models](#trained-models)
- [Image annotation tools](#image-annotation-tools)
- [Citation](#citation)
- [Contributing](#contributing)
- [License](#license)
- [Issues](#issues)
## Installation
There are two ways to install `napari-aisegcell`: First, you can install `napari-aisegcell` from command line. Second, you
have `napari` already installed as a graphical user interface (GUI) and install `napari-aisegcell` from the GUI menu.
We recommend the [command line](#command-line) installation as it provides fine-grained control of the
installation to prevent conflicts with existing napari plugins. Use the [one-click](#one-click) installation if
you do not want to concern yourself with virtual environments or the command line. Just be aware that using the
[one-click](#one-click) installation may introduce conflicts with already installed plugins or new plugin installations
may disrupt this plugin.
`napari-aisegcell` was tested with
```bash
OS = macOS 12.6.3/ubuntu 22.10/windows 10
python = 3.8.6
torch = 1.10.2
torchvision = 0.11.3
pytorch-lightning = 1.5.9
```
### Command line
`napari` must be installed from command line to install `napari aisegcell` from command line. Installation requires a
command line application (e.g. `Terminal`) with [git] and [python] installed. If you do not have python installed
already, we recommend installing it using the [Anaconda distribution](https://www.anaconda.com/products/distribution).
If you operate on `Windows` we recommend using `Anaconda Powershell Prompt` as command line application.
An introductory tutorial on how to use `git` and GitHub can be found
[here](https://www.w3schools.com/git/default.asp?remote=github).
1) (Optional) If you already [installed napari](https://napari.org/stable/#installation) in a
[virtual environment](https://realpython.com/python-virtual-environments-a-primer/) you can skip this step.
However, you may want to install `napari-aisegcell` in a fresh environment to avoid conflicts with existing
plugins. Create a new virtual environment for `napari`. [Here](https://testdriven.io/blog/python-environments/) is a
list of different python virtual environment tools. Open your command line application and create a (e.g. `conda`)
virtual environment
```bash
conda create -n napari python=3.8
```
2) Activate your virtual environment that has `napari` installed or you want to install `napari` to
```bash
conda activate napari
```
3) (Optional) Install `napari`. Skip this step if you have `napari` already installed.
```bash
pip install "napari[all]"
```
3) (Optional) If you use `Anaconda Powershell Prompt`, install `git` through `conda`
```bash
conda install -c anaconda git
```
4) Install `napari-aisegcell`
1) from [PyPI]
```bash
pip install napari-aisegcell
```
2) from GitHub (= latest development version)
```bash
pip install git+https://github.com/CSDGroup/napari-aisegcell.git
```
With step 4) completed you have successfully installed `napari-aisegcell`. You can proceed with the
[documentation](#documentation) on how to use `napari-aisegcell`. *NOTE*, that when opening the plugin for the
first time, the remaining dependencies (`torch, torchvision, pytorch-lightning`) will be automatically installed
via [light-the-torch](https://github.com/pmeier/light-the-torch). If you prefer to manually install the remaining
dependencies (i.e. prevent potential interference with your virtual environment), proceed with step 5).
5) (Optional) `GPUs` greatly speed up training and inference of [aisegcell] and are available for `torch` (`v1.10.2`)
for `Windows` and `Linux`. Check if your `GPU(s)` are CUDA compatible
([`Windows`](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/#verify-you-have-a-cuda-capable-gpu),
[`Linux`](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#verify-you-have-a-cuda-capable-gpu)) and
update their drivers if necessary.
6) (Optional) [Install `torch`/`torchvision`](https://pytorch.org/get-started/previous-versions/) compatible with your
system. `aisegcell` was tested with `torch` version `1.10.2`, `torchvision` version `0.11.3`, and `cuda` version
`11.3.1`. Depending on your OS, your `CPU` or `GPU` (and `CUDA` version) the installation may change
```bash
# Windows/Linux CPU
pip install torch==1.10.2+cpu torchvision==0.11.3+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
# Windows/Linux GPU (CUDA 11.3.X)
pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
# macOS CPU
pip install torch==1.10.2 torchvision==0.11.3
```
7) (Optional) [Install `pytorch-lightning`](https://www.pytorchlightning.ai). `aisegcell` was tested with
version `1.5.9`.
```bash
# note the installation of v1.5.9 does not use pip install lightning
pip install pytorch-lightning==1.5.9
```
### One-click
(*NOT YET AVAILABLE*) Using the one-click installation of `napari-aisegcell` is as easy as opening `napari`, selecting
`Plugins>Install/Uninstall Plugins...` and searching for `napari-aisegcell` in the search bar. Select `install` and
restart `napari` for `napari-aisegcell` to appear in the list of installed plugins in the `Plugins` menu. Please
recall that one-click installing `napari-aisegcell` may interfere with existing plugin installations or new
plugin installations may interfere with the `napari-aisegcell` plugin.
## Data
`napari-aisegcell` is currently intended for single-class semantic and instance segmentation. Input images are expected
to be 8-bit or 16-bit greyscale images. Segmentation masks are expected to decode background as 0 intensity and all intensities
\>0 are converted to a single intensity value (255). Have a look at
[this notebook](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/data_example.ipynb)
for a data example.
## Documentation
`napari-aisegcell` has two modes: The [layer mode](#layer-mode) and the [batch mode](#batch-mode). Layer mode is
intended to explore if existing trained models in `napari-aisegcell` are suitable for your data. Batch mode is
intended for high-throughput image segmentation once you have confirmed that existing models are well suited for
your data.
### First launch
The first time you launch `napari-aisegcell`, `torch, torchvision, pytorch-lightning` will be automatically
installed if you have skipped steps 5)-7) of the [installation](#installation). The napari window will freeze
during download and installation. Depending on your setup this may take several minutes (~GBs of download). Similarly,
the first time you are running a pre-trained model the model weights (.CKPT file) will be downloaded and will
delay the prediction (~MBs of download).
### Launch napari
The `napari` bundled app is launched by opening the desktop shortcut. To launch napari as a command line
application
1) Open the terminal and activate the environment you installed `napari-aisegcell` into
```bash
conda activate napari
```
2) Run `napari` in terminal
```bash
napari
```
### Layer mode
Open the layer mode in the menu `Plugins>napari-aisegcell>Layer mode`. Select the parameters you want to use to
obtain your segmentation and select the `Run` button.
#### Data section
In the Data section (magenta) you can select the images you want to segment from a drop-down menu. Only images that are
loaded in the `napari` [layer tab](https://napari.org/stable/guides/layers.html) are available for selection.
Image formats must be readable by [`skimage.io.imread`](https://scikit-image.org/docs/stable/api/skimage.io.html#skimage.io.imread).
#### Neural network section
In the neural network section (cyan) you can select the neural net you want to use for segmentation. The section
has three parameters
- `Model type`
- `NucSeg`: Select this option to use a pre-trained [aisegcell] model to segment nuclei (see [trained models](#trained-models)).
- `CellSeg`: Select this option to use a pre-trained [aisegcell] model to segment whole cells (see [trained models](#trained-models)).
- `Custom`: Select this option if you want to load a [aisegcell] model that does not ship with `napari-aisegcell`.
Custom models can be obtained by training your own [aisegcell] model or obtaining [aisegcell] models from 3rd
parties. You must select the checkpoint (.ckpt) file in the emerging `Custom Model` parameter.
- `Pre-trained Model`: Drop-down menu to select the available pre-trained models for `NucSeg` or `CellSeg`.
- `Bright Field`: a model to segment nuclei/whole cells in bright field. Currently, no other image modalities
are available.
- `Computing Device`: Drop-down menu that lists computing devices (CPU/GPUs) available with your current `torch`
installation.
#### Post-processing section
In the post-processing section (green) a selection of common post-processing steps are available
- `Instance Segmentation`: Check this box to return instance segmentations instead of semantic segmentations.
- `Remove Holes <`: Removes holes in objects (e.g. nuclei) \<X pixels.
- `Minimum object size`: Removes objects of size \<X pixels before the dilation step.
- `Maximum object size`: Removes objects of size \>X pixels before the dilation step.
- `Dilation`: Dilate (\>0) or erode (\<0) objects by X pixels (see [here](https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html)).
### Batch mode
Open the batch mode in the menu `Plugins>napari-aisegcell>Batch mode`. Select the parameters you want to use to
obtain your segmentations and select the `Run` button.
The [neural network section](#neural-network-section) and [post-processing-section](#post-processing-section) are
the same as in the layer mode.
#### Data section
In the data section (magenta) you can select the images you want to submit for batch processing.
- `Input`
- `Select file`: Select an existing input CSV file that follows the [aisegcell input format](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/unet_example.ipynb)
- `Create file`: Create a CSV file that follows the [aisegcell input format](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/unet_example.ipynb)
- `Input directory`: Select parent directory containing all images to segment. Images can be in subdirectories.
- `File pattern`: All files matching this pattern in your selected `Input directory` will be stored in the CSV file.
Use [wildcard characters](https://linuxhint.com/bash_wildcard_tutorial/) like `*` to capture all images
you want to segment in one run.
- Example 1 `*/*.png`: will select all `PNG` files in in all sub-directories of `Input directory`.
- Example 2 `position*z1.png`: will select all files in `Input directory` that start with "position" and
end with "z1.png"
- `Save as`: Name (and path) of the file that will be the input to your selected Neural Network. The file
can be used as input to `Select file`.
- `Mask suffix`: Mask suffix that will be appended to each mask file name.
- Example `suffix = "_mask"`: `my_image.png` -> `my_image_mask.png`
- `Output`:
- `Directory`: Directory to which all segmentation masks will be saved. Be aware that input images with
identical file names will be appended with `Mask suffix` AND an ID.
- `CSD format`: Store segmentation masks following the storage system of the [Cell Systems Dynamics group](https://bsse.ethz.ch/csd).
CSD format finds the deepest common directory of all input images, creates folders 'Analysis/Segmentation_YYMMDD',
and reconstructs the unique parts of all input paths in "Segmentation_YYMMDD".
### Trained models
We provide trained models:
| modality | image format | model | example image | description | availability |
| :-- | :-: | :-: | :-: | :-: | :-- |
| bright field nucleus segmentation | 2D grayscale | U-Net | <img src="https://github.com/CSDGroup/aisegcell/raw/main/images/nucseg.png" title="example nucleus segmentation" width="180px" align="center"> | Trained on a data set (link to data set) of 9849 images (~620k nuclei). | [ETH Research Collection](https://www.research-collection.ethz.ch/handle/20.500.11850/608641) |
| bright field whole cell segmentation | 2D grayscale | U-Net | <img src="https://github.com/CSDGroup/aisegcell/raw/main/images/cellseg.png" title="example whole cell segmentation" width="180px" align="center"> | Trained on a data set (link to data set) of 224 images (~12k cells). | [ETH Research Collection](https://www.research-collection.ethz.ch/handle/20.500.11850/608646) |
## Image annotation tools
Available tools to annotate segmentations include:
- [napari](https://napari.org/stable/)
- [Labkit](https://imagej.net/plugins/labkit/) for [Fiji](https://imagej.net/software/fiji/downloads)
- [QuPath](https://qupath.github.io)
- [ilastik](https://www.ilastik.org)
## Citation
t.b.d.
## 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-aisegcell" is free and open source software
## Issues
If you encounter any problems, please [file an issue] along with a detailed description.
[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[file an issue]: https://github.com/CSDGroup/napari-aisegcell/issues
[aisegcell]: https://github.com/CSDGroup/aisegcell
[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git
[tox]: https://tox.readthedocs.io/en/latest/
[PyPI]: https://pypi.org/
[python]: https://www.python.org
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"description": "# napari-aiSEGcell\n\n[](https://github.com/CSDGroup/napari-aisegcell/raw/main/LICENSE)\n[](https://pypi.org/project/napari-aisegcell)\n[](https://python.org)\n[](https://github.com/CSDGroup/napari-aisegcell/actions)\n[](https://codecov.io/gh/CSDGroup/napari-aisegcell)\n[](https://napari-hub.org/plugins/napari-aisegcell)\n\nA [napari] plugin to segment cell nuclei and whole cells in bright field microscopy images. `napari-aisegcell` uses\n[aisegcell] for segmentation. This plugin can only be used to use already trained models for segmentation. If you\nwant to train a new model use [aisegcell]. Please cite [this paper](#citation) if you are using this plugin in your \nresearch.\n\n\n\n----------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n<!--\nDon't miss the full getting started guide to set up your new package:\nhttps://github.com/napari/cookiecutter-napari-plugin#getting-started\n\nand review the napari docs for plugin developers:\nhttps://napari.org/stable/plugins/index.html\n-->\n\n## Contents\n - [Installation](#installation)\n - [Command line](#command-line)\n - [One-click](#one-click)\n - [Data](#data)\n - [Documentation](#documentation)\n - [First launch](#first-launch)\n - [Launch napari]((#launch-napari))\n - [Layer mode](#layer-mode)\n - [Batch mode](#batch-mode)\n - [Trained models](#trained-models)\n - [Image annotation tools](#image-annotation-tools)\n - [Citation](#citation)\n - [Contributing](#contributing)\n - [License](#license)\n - [Issues](#issues)\n\n## Installation\nThere are two ways to install `napari-aisegcell`: First, you can install `napari-aisegcell` from command line. Second, you\nhave `napari` already installed as a graphical user interface (GUI) and install `napari-aisegcell` from the GUI menu.\n\nWe recommend the [command line](#command-line) installation as it provides fine-grained control of the\ninstallation to prevent conflicts with existing napari plugins. Use the [one-click](#one-click) installation if\nyou do not want to concern yourself with virtual environments or the command line. Just be aware that using the\n[one-click](#one-click) installation may introduce conflicts with already installed plugins or new plugin installations\nmay disrupt this plugin.\n\n`napari-aisegcell` was tested with\n```bash\nOS = macOS 12.6.3/ubuntu 22.10/windows 10\npython = 3.8.6\ntorch = 1.10.2\ntorchvision = 0.11.3\npytorch-lightning = 1.5.9\n```\n\n### Command line\n`napari` must be installed from command line to install `napari aisegcell` from command line. Installation requires a\ncommand line application (e.g. `Terminal`) with [git] and [python] installed. If you do not have python installed\nalready, we recommend installing it using the [Anaconda distribution](https://www.anaconda.com/products/distribution).\nIf you operate on `Windows` we recommend using `Anaconda Powershell Prompt` as command line application.\nAn introductory tutorial on how to use `git` and GitHub can be found\n[here](https://www.w3schools.com/git/default.asp?remote=github).\n\n1) (Optional) If you already [installed napari](https://napari.org/stable/#installation) in a\n[virtual environment](https://realpython.com/python-virtual-environments-a-primer/) you can skip this step.\nHowever, you may want to install `napari-aisegcell` in a fresh environment to avoid conflicts with existing\nplugins. Create a new virtual environment for `napari`. [Here](https://testdriven.io/blog/python-environments/) is a\nlist of different python virtual environment tools. Open your command line application and create a (e.g. `conda`)\nvirtual environment\n\n ```bash\n conda create -n napari python=3.8\n ```\n\n2) Activate your virtual environment that has `napari` installed or you want to install `napari` to\n\n ```bash\n conda activate napari\n ```\n\n3) (Optional) Install `napari`. Skip this step if you have `napari` already installed.\n\n ```bash\n pip install \"napari[all]\"\n ```\n\n3) (Optional) If you use `Anaconda Powershell Prompt`, install `git` through `conda`\n\n ```bash\n conda install -c anaconda git\n ```\n\n4) Install `napari-aisegcell`\n\n 1) from [PyPI]\n\n ```bash\n pip install napari-aisegcell\n ```\n 2) from GitHub (= latest development version)\n\n ```bash\n pip install git+https://github.com/CSDGroup/napari-aisegcell.git\n ```\n\nWith step 4) completed you have successfully installed `napari-aisegcell`. You can proceed with the\n[documentation](#documentation) on how to use `napari-aisegcell`. *NOTE*, that when opening the plugin for the\nfirst time, the remaining dependencies (`torch, torchvision, pytorch-lightning`) will be automatically installed\nvia [light-the-torch](https://github.com/pmeier/light-the-torch). If you prefer to manually install the remaining\ndependencies (i.e. prevent potential interference with your virtual environment), proceed with step 5).\n\n5) (Optional) `GPUs` greatly speed up training and inference of [aisegcell] and are available for `torch` (`v1.10.2`) \nfor `Windows` and `Linux`. Check if your `GPU(s)` are CUDA compatible\n([`Windows`](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/#verify-you-have-a-cuda-capable-gpu),\n [`Linux`](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#verify-you-have-a-cuda-capable-gpu)) and\n update their drivers if necessary.\n\n6) (Optional) [Install `torch`/`torchvision`](https://pytorch.org/get-started/previous-versions/) compatible with your\nsystem. `aisegcell` was tested with `torch` version `1.10.2`, `torchvision` version `0.11.3`, and `cuda` version\n`11.3.1`. Depending on your OS, your `CPU` or `GPU` (and `CUDA` version) the installation may change\n\n```bash\n# Windows/Linux CPU\npip install torch==1.10.2+cpu torchvision==0.11.3+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html\n\n# Windows/Linux GPU (CUDA 11.3.X)\npip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n\n# macOS CPU\npip install torch==1.10.2 torchvision==0.11.3\n\n```\n\n7) (Optional) [Install `pytorch-lightning`](https://www.pytorchlightning.ai). `aisegcell` was tested with\nversion `1.5.9`.\n\n```bash\n# note the installation of v1.5.9 does not use pip install lightning\npip install pytorch-lightning==1.5.9\n```\n\n### One-click\n(*NOT YET AVAILABLE*) Using the one-click installation of `napari-aisegcell` is as easy as opening `napari`, selecting\n`Plugins>Install/Uninstall Plugins...` and searching for `napari-aisegcell` in the search bar. Select `install` and\nrestart `napari` for `napari-aisegcell` to appear in the list of installed plugins in the `Plugins` menu. Please\nrecall that one-click installing `napari-aisegcell` may interfere with existing plugin installations or new\nplugin installations may interfere with the `napari-aisegcell` plugin.\n\n## Data\n`napari-aisegcell` is currently intended for single-class semantic and instance segmentation. Input images are expected \nto be 8-bit or 16-bit greyscale images. Segmentation masks are expected to decode background as 0 intensity and all intensities\n\\>0 are converted to a single intensity value (255). Have a look at\n[this notebook](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/data_example.ipynb)\nfor a data example.\n\n## Documentation\n`napari-aisegcell` has two modes: The [layer mode](#layer-mode) and the [batch mode](#batch-mode). Layer mode is\nintended to explore if existing trained models in `napari-aisegcell` are suitable for your data. Batch mode is\nintended for high-throughput image segmentation once you have confirmed that existing models are well suited for\nyour data.\n\n### First launch\nThe first time you launch `napari-aisegcell`, `torch, torchvision, pytorch-lightning` will be automatically\ninstalled if you have skipped steps 5)-7) of the [installation](#installation). The napari window will freeze\nduring download and installation. Depending on your setup this may take several minutes (~GBs of download). Similarly,\nthe first time you are running a pre-trained model the model weights (.CKPT file) will be downloaded and will\ndelay the prediction (~MBs of download).\n\n### Launch napari\nThe `napari` bundled app is launched by opening the desktop shortcut. To launch napari as a command line\napplication\n\n1) Open the terminal and activate the environment you installed `napari-aisegcell` into\n\n ```bash\n conda activate napari\n ```\n2) Run `napari` in terminal\n\n ```bash\n napari\n ```\n\n### Layer mode\nOpen the layer mode in the menu `Plugins>napari-aisegcell>Layer mode`. Select the parameters you want to use to\nobtain your segmentation and select the `Run` button.\n\n\n\n#### Data section\nIn the Data section (magenta) you can select the images you want to segment from a drop-down menu. Only images that are\nloaded in the `napari` [layer tab](https://napari.org/stable/guides/layers.html) are available for selection.\nImage formats must be readable by [`skimage.io.imread`](https://scikit-image.org/docs/stable/api/skimage.io.html#skimage.io.imread).\n\n#### Neural network section\nIn the neural network section (cyan) you can select the neural net you want to use for segmentation. The section\nhas three parameters\n\n - `Model type`\n - `NucSeg`: Select this option to use a pre-trained [aisegcell] model to segment nuclei (see [trained models](#trained-models)).\n - `CellSeg`: Select this option to use a pre-trained [aisegcell] model to segment whole cells (see [trained models](#trained-models)).\n - `Custom`: Select this option if you want to load a [aisegcell] model that does not ship with `napari-aisegcell`.\n Custom models can be obtained by training your own [aisegcell] model or obtaining [aisegcell] models from 3rd\n parties. You must select the checkpoint (.ckpt) file in the emerging `Custom Model` parameter.\n - `Pre-trained Model`: Drop-down menu to select the available pre-trained models for `NucSeg` or `CellSeg`.\n - `Bright Field`: a model to segment nuclei/whole cells in bright field. Currently, no other image modalities\n are available.\n - `Computing Device`: Drop-down menu that lists computing devices (CPU/GPUs) available with your current `torch`\n installation. \n\n#### Post-processing section\nIn the post-processing section (green) a selection of common post-processing steps are available\n\n - `Instance Segmentation`: Check this box to return instance segmentations instead of semantic segmentations.\n - `Remove Holes <`: Removes holes in objects (e.g. nuclei) \\<X pixels.\n - `Minimum object size`: Removes objects of size \\<X pixels before the dilation step.\n - `Maximum object size`: Removes objects of size \\>X pixels before the dilation step.\n - `Dilation`: Dilate (\\>0) or erode (\\<0) objects by X pixels (see [here](https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html)).\n\n### Batch mode\nOpen the batch mode in the menu `Plugins>napari-aisegcell>Batch mode`. Select the parameters you want to use to\nobtain your segmentations and select the `Run` button.\n\nThe [neural network section](#neural-network-section) and [post-processing-section](#post-processing-section) are\nthe same as in the layer mode.\n\n\n\n#### Data section\nIn the data section (magenta) you can select the images you want to submit for batch processing.\n\n - `Input`\n - `Select file`: Select an existing input CSV file that follows the [aisegcell input format](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/unet_example.ipynb)\n - `Create file`: Create a CSV file that follows the [aisegcell input format](https://github.com/CSDGroup/aisegcell/blob/main/notebooks/unet_example.ipynb)\n - `Input directory`: Select parent directory containing all images to segment. Images can be in subdirectories.\n - `File pattern`: All files matching this pattern in your selected `Input directory` will be stored in the CSV file.\n Use [wildcard characters](https://linuxhint.com/bash_wildcard_tutorial/) like `*` to capture all images\n you want to segment in one run.\n - Example 1 `*/*.png`: will select all `PNG` files in in all sub-directories of `Input directory`.\n - Example 2 `position*z1.png`: will select all files in `Input directory` that start with \"position\" and\n end with \"z1.png\"\n - `Save as`: Name (and path) of the file that will be the input to your selected Neural Network. The file\n can be used as input to `Select file`.\n - `Mask suffix`: Mask suffix that will be appended to each mask file name.\n - Example `suffix = \"_mask\"`: `my_image.png` -> `my_image_mask.png`\n - `Output`:\n - `Directory`: Directory to which all segmentation masks will be saved. Be aware that input images with \n identical file names will be appended with `Mask suffix` AND an ID. \n - `CSD format`: Store segmentation masks following the storage system of the [Cell Systems Dynamics group](https://bsse.ethz.ch/csd).\n CSD format finds the deepest common directory of all input images, creates folders 'Analysis/Segmentation_YYMMDD', \n and reconstructs the unique parts of all input paths in \"Segmentation_YYMMDD\".\n\n### Trained models\nWe provide trained models:\n\n| modality | image format | model | example image | description | availability |\n| :-- | :-: | :-: | :-: | :-: | :-- |\n| bright field nucleus segmentation | 2D grayscale | U-Net | <img src=\"https://github.com/CSDGroup/aisegcell/raw/main/images/nucseg.png\" title=\"example nucleus segmentation\" width=\"180px\" align=\"center\"> | Trained on a data set (link to data set) of 9849 images (~620k nuclei). | [ETH Research Collection](https://www.research-collection.ethz.ch/handle/20.500.11850/608641) |\n| bright field whole cell segmentation | 2D grayscale | U-Net | <img src=\"https://github.com/CSDGroup/aisegcell/raw/main/images/cellseg.png\" title=\"example whole cell segmentation\" width=\"180px\" align=\"center\"> | Trained on a data set (link to data set) of 224 images (~12k cells). | [ETH Research Collection](https://www.research-collection.ethz.ch/handle/20.500.11850/608646) |\n\n\n## Image annotation tools\nAvailable tools to annotate segmentations include:\n\n - [napari](https://napari.org/stable/)\n - [Labkit](https://imagej.net/plugins/labkit/) for [Fiji](https://imagej.net/software/fiji/downloads)\n - [QuPath](https://qupath.github.io)\n - [ilastik](https://www.ilastik.org)\n\n## Citation\nt.b.d.\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-aisegcell\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\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[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[file an issue]: https://github.com/CSDGroup/napari-aisegcell/issues\n\n[aisegcell]: https://github.com/CSDGroup/aisegcell\n[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\n[tox]: https://tox.readthedocs.io/en/latest/\n[PyPI]: https://pypi.org/\n[python]: https://www.python.org\n",
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