vollseg-napari
Irregular cell shape segmentation using VollSeg
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)…
. If you want GPU-accelerated prediction, please read the more detailed installation instructions for VollSeg.
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.
GPU_Installation¶
This package is compatible with Python 3.6 - 3.9.
-
Please first install TensorFlow (TensorFlow 2) by following the official instructions. For GPU support, it is very important to install the specific versions of CUDA and cuDNN that are compatible with the respective version of TensorFlow. (If you need help and can use
conda
, take a look at this.) -
VollSeg can then be installed with
pip
:-
If you installed TensorFlow 2 (version 2.x.x):
pip install vollseg
-
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 | GT image | Optimal combination | Notebook Code | Model Prediction | Metrics |
---|---|---|---|---|---|---|---|---|
![]() | Light sheet fused from four angles 3D single channel | Training Data ~320 GB | UNET model | ![]() | UNET model, slice_merge = False | Colab Notebook | ![]() | ![]() |
![]() | Confocal microscopy 3D single channel 8 bit | Training Data | Denoising Model and StarDist Model | ![]() | StarDist model + Denoising Model, dounet = False | Colab Notebook | ![]() | ![]() |
![]() | LaserScanningConfocalMicroscopy 2D single channel | Dataset | UNET Model | ![]() | UNET model | Colab Notebook | ![]() | No Metrics |
![]() | TIRF + MultiKymograph Fiji tool 2D single channel | Training Dataset | UNET Model | ![]() | UNET model | Colab Notebook | ![]() | No Metrics |
![]() | XRay of Lung 2D single channel | Training Dataset | UNET Model | ![]() | UNET model | Colab Notebook | ![]() | ![]() |
![]() | LaserScanningConfocalMicroscopy 2D single channell | Test Dataset | Private | ![]() | UNET model | Colab Notebook | ![]() | No metrics |
![]() | LaserScanningConfocalMicroscopy 3D single channell | Test Dataset | Private | ![]() | UNET model + StarDist model + ROI model | Colab Notebook | ![]() | ![]() |
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.
Version:
- 2.3.7
Last updated:
- 03 October 2022
First released:
- 10 December 2021
License:
- BSD-3-Clause
Supported data:
Plugin type:
GitHub activity:
- Stars: 11
- Forks: 1
- Issues + PRs: 0
GitHub activity:
- Stars: 11
- Forks: 1
- Issues + PRs: 0
Requirements:
- vollseg
- napari (>=0.4.13)
- magicgui (>=0.4.0)
- tensorflow ; platform_system != "Darwin" or platform_machine != "arm64"
- tensorflow-macos ; platform_system == "Darwin" and platform_machine == "arm64"