VollSeg Napari Plugin#
VollSeg is more than just a single segmentation algorithm; it is a meticulously designed modular segmentation tool tailored to diverse model organisms and imaging methods. While a U-Net might suffice for certain image samples, others might benefit from utilizing StarDist, and some could require a blend of both, potentially coupled with denoising or region of interest models. The pivotal decision left to make is how to select the most appropriate VollSeg configuration for your dataset, a question we comprehensively address in our documentation website.
This project provides the napari plugin for VollSeg, a deep learning based 2D and 3D segmentation tool for irregular shaped cells. VollSeg has originally been developed (see papers) for the segmentation of densely packed membrane labelled cells in challenging images with low signal-to-noise ratios. The plugin allows to apply pretrained and custom trained models from within napari. For detailed demo of the plugin see these videos and a short video about the parameter selection
Installation & Usage#
Install the plugin with pip install vollseg-napari or from within napari via Plugins > Install/Uninstall Package(s)….
You can activate the plugin in napari via Plugins > VollSeg: VollSeg. Example images for testing are provided via File > Open Sample > VollSeg.
If you use this plugin for your research, please cite us.
Examples#
VollSeg comes with different options to combine CARE based denoising with UNET, StarDist and segmentation in a region of interest (ROI). We present some examples which are represent optimal combination of these different modes for segmenting different cell types. We summarize this in the table below:
Example Image |
Description |
Training Data |
Trained Model |
|---|---|---|---|
|
Light sheet fused from four angles 3D single channel |
||
|
Confocal microscopy 3D single channel 8 bit |
||
|
LaserScanningConfocalMicroscopy 2D single channel |
Troubleshooting & Support#
The image.sc forum is the best place to start getting help and support. Make sure to use the tag
vollseg, since we are monitoring all questions with this tag.If you have technical questions or found a bug, feel free to open an issue.