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 package can be installed by

pip install vollseg

If you are building this from the source, clone the repository and install via

git clone https://github.com/kapoorlab/vollseg/

cd vollseg

pip install -e .

• Algorithm
  • Schematic representation showing the segmentation approach used in VollSeg.
  • First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise.
  • Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labeled via connected components (C).
  • We then obtain seeds from the centroids of labeled image in B, for each labeled region of C in order to create bounding boxes and centroids.
  • If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D).
  • Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain the final cell segmentation result shown in (F).
  • All images are displayed in Napari viewer with 3D display view.

• Python 3.7 and above.

Under MIT license. See LICENSE.

• Varun Kapoor randomaccessiblekapoor@gmail.com
• Claudia Carabaña
• Mari Tolonen