# Light Beads Microscopy (LBM) Pipeline: Suite2p
[](https://pypi.org/project/lbm-suite2p-python/)
[](https://millerbrainobservatory.github.io/LBM-Suite2p-Python/index.html)
This package is still in a *late-beta* stage of development.
A pipeline for processing volumetric 2-photon Light Beads Microscopy (LBM) datasets.
This pipeline uses the following open-source software:
- [suite2p](https://github.com/MouseLand/suite2p)
- [cellpose](https://github.com/MouseLand/cellpose)
- [rastermap](https://github.com/MouseLand/rastermap)
- [mbo_utilities](https://github.com/MillerBrainObservatory/mbo_utilities)
- [scanreader](https://github.com/atlab/scanreader)
[](https://doi.org/10.1038/s41592-021-01239-8)
---
## Installation
This pipeline is installable with `pip`:
```bash
pip install lbm_suite2p_python
# with uv: uv pip install lbm_suite2p_python
```
We highly encourage the use of a virtual environment. If you are unfamiliar with virtual environments, see our documentation [here](https://millerbrainobservatory.github.io/mbo_utilities/venvs.html).
You may also use git to clone and install locally for updates not yet released to pypi:
```bash
git clone https://github.com/MillerBrainObservatory/LBM-Suite2p-Python.git
cd LBM-Suite2p-Python
# make sure your virtual environment is active
pip install "."
```
## Features
### 2.0.0
- Process ScanImage multi-ROI as separate datasets
- Post-processing cell filters for area, exceptional events and eccentricity
### 1.0.0
- Suite2p planar segmentation
- DF/F, baseline calculation and documentation
- Aggregate planar outputs into volumetric dataset
## Issues
Widgets may throw "Invalid Rect" errors. This can be safely ignored until it is [resolved](https://github.com/pygfx/wgpu-py/issues/716#issuecomment-2880853089).
---
## Acknowledgements
This pipeline is mostly a volumetric wrapper around [suite2p](https://github.com/MouseLand/suite2p), [cellpose](https://github.com/MouseLand/cellpose) and [Suite3D](https://github.com/alihaydaroglu/suite3d). We thank the contributors to those projects.
Thank you to the developers of [scanreader](https://github.com/atlab/scanreader), which provides a clean interface to ScanImage metadata using only tifffile and numpy.
Raw data
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