VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs

     

             

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VesselExpress is an open-source software designed for rapid, fully automated and scalable analysis of vascular datasets in high-throughput sequences. It processes raw microscopic images (2D or 3D) of blood vessels in parallel and outputs quantified phenotypical data along with image and object files of the rendered vasculature. The processing steps include segmentation, skeletonization, graph construction with analysis and optional rendering. These steps are automated in a pipeline with the workflow management system Snakemake (see workflow DAG). The whole pipeline can be run via Docker or locally from the command line or web browser using the web interface.

 

Notes: It is also possible to execute each step individually with the corresponding Python script. Existing modules can be exchanged with custom scripts in the Snakefile.

Tutorial Video

You can use this YouTube Video for visualized instructions on how to download, setup and run VesselExpress on example data. By clicking on the image, you will be redirected to an external webseite to view the video. Learn more about the video here.

 

Docker Version

Browser-Based User Interface	Command-Line only

If you want to use the browser version, follow these instructions:

1. Install Docker for your operating system from here.
2. Start Docker.
3. Get the Docker image
   1. from DockerHub via docker pull philippasp/vesselexpress
   2. or build the Docker image by calling docker build -f ./Webinterface/Dockerfile -t vesselexpress .
4. Run VesselExpress via
   1. docker run -p 5000:5000 philippasp/vesselexpress if you've pulled the image from DockerHub
   2. or via docker run -p 5000:5000 vesselexpress if you've manually built the image as described above.
5. Navigate to localhost:5000 in your browser (e.g. Firefox or Google Chrome) to open the web interface. On macOS Monterey you need to uncheck "AirPlay Receiver" in the "Sharing" system preference to use the web interface on port 5000.

If you want to use the command-line interface, follow these instructions:

1. Install Docker for your operating system from here.
2. Start Docker.
3. Get the Docker image
   1. from DockerHub via docker pull philippasp/vesselexpress_cli
   2. by calling docker build -t vesselexpress_cli . from the VesselExpress directory.
4. Run VesselExpress
   1. via docker run -v path-to-data-and-config:/home/user/VesselExpress/data philippasp/vesselexpress_cli if you've pulled the image from DockerHub
   2. or via docker run -v path-to-data-and-config:/home/user/VesselExpress/data vesselexpress_cli if you've manually built the image as described above.
      The first part of the command specifies the absolute path on your host containing image files to process and the configuration file. An example configuration file and 3D image can be found in the data folder. By default VesselExpress uses all cores for computation. We recommend decreasing the number of cores when working on an office PC to avoid out of memory errors. You can specify the number of cores via the flag -e. To run VesselExpress with 4 cores use docker run -v path-to-data-and-config:/home/user/VesselExpress/data -e 'CORES=4' vesselexpress_cli

Local Version (without Docker)

We recommend using the Docker version. The local version is intended for development. So far we have tested this on Ubuntu and macOS (excluding Frangi-Net). If you're using Windows, please use the Docker version.

For the browser version follow these instructions:

1. Install Blender (optional for rendering).
   We used Blender 2.83.4 on Linux and Blender 2.83.5 on macOS. You might need to adjust the path to Blender on your system in the Snakefile.
2. Download all packages via pip install -r Webinterface/requirements.txt.
3. Type python Webinterface/server/app.py to run the web application.
4. In your browser navigate to localhost:5000 to open the webpage.

For the command-line version follow these instructions:

1. Install Blender from here (see descriptions above).
2. Install Anaconda following the installation instructions.
3. Install Snakemake following the installation instructions.
4. In a terminal navigate to the project's root folder and type snakemake --use-conda --cores all --conda-frontend conda.

Different parameters can be set by using the command line option '--config' or by changing the parameters in the config.json file. A full description of command line arguments for Snakemake can be found here.

You can learn more about how to set up and run VesselExpress here.

Wiki

There is a lot of useful information in our wiki.

Two important pages are:

• Our FAQ
• Our detailed overview over VesselExpress' parameters for a customized execution.

Correspondence

Prof. Dr. Axel Mosig: Bioinformatics, Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, Bochum, Germany

http://www.bioinf.rub.de/

Prof. Dr. Matthias Gunzer: Institute for Experimental Immunology and Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany

https://www.uni-due.de/experimental-immunology

Example Data

Download our example data from Zenodo containing 3D light-sheet microscopic images of different organs with corresponding preset parameters for each organ. For segmentation parameter tuning you can use our Napari plugin.

Feedback & Bug Reports

We strive to always improve and make this pipeline accessible to the public. We hope to make it as easy to use as possible. For your convinience we have set up a FAQ here.

Should you encounter an error, bug or need help, please feel free to reach out to us via the Issues page. Thank you for your help. Your feedback is much appreciated. 👍

References

1. Chen, J., et al. The Allen Cell Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images. bioRxiv, 491035 (2018)
2. Fu, W. Frangi-Net on High-Resolution Fundus (HRF) image database. (2019). https://doi.org/10.24433/CO.5016803.v2
3. Pesavento, M & Vemuri, P. 3D Image Skeletonization Tools. (2019). https://github.com/pranathivemuri/skeletonization/commit/b7bd1ce06e557905a32307677c77c1b94305ba5c
4. Bumgarner, J., Nelson, R. Open-source analysis and visualization of segmented vasculature datasets with VesselVio. Cell Reports Methods (2022), https://doi.org/10.1016/j.crmeth.2022.100189.

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Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict of interest.

Corresponding publication: https://doi.org/10.1016/j.crmeth.2023.100436