stardist

3D segmentation of secretory granules with 3D stardist

The code is this folder demonstrates how to train a Stardist model to segment secretory granules from 3D FIB-SEM data as described in the paper:

Müller, Andreas, et al. "3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse β cells." Journal of Cell Biology 220.2 (2021).

For general question regarding those parameters, please see https://github.com/stardist/stardist.

1. Install tensorflow with gpu support

2. Install stardist and dependencies:

   • pip install stardist tqdm
   • pip install git+https://github.com/stardist/augmend.git

3. Download the example data (or adapt your own data into the same format)

   • wget https://syncandshare.desy.de/index.php/s/5SJFRtAckjBg5gx/download/data_granules.zip
   • unzip data_granules.zip

   which should result in the following folder structure:

   data_granules
   ├── train
   │   ├── images
   │   └── masks
   └── val
       ├── images
       └── masks
   

Usage

Simply run the notebook for training a stardist model and applying it on new stacks.