Skip to main content

Irregular cell shape segmentation using VollSeg

Project description

VollSeg Napari Plugin

PyPI version

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.

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.

  1. 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.)

  2. 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

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vollseg-napari-2.1.7.tar.gz (43.8 kB view hashes)

Uploaded Source

Built Distribution

vollseg_napari-2.1.7-py3-none-any.whl (42.5 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page