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StarDist - Object Detection with Star-convex Shapes

This repository contains the implementation of star-convex object detection for 2D and 3D images, as described in the papers:

Please cite the paper(s) if you are using this code in your research.


The following figure illustrates the general approach for 2D images. The training data consists of corresponding pairs of input (i.e. raw) images and fully annotated label images (i.e. every pixel is labeled with a unique object id or 0 for background). A model is trained to densely predict the distances (r) to the object boundary along a fixed set of rays and object probabilities (d), which together produce an overcomplete set of candidate polygons for a given input image. The final result is obtained via non-maximum supression (NMS) of these candidates.

The approach for 3D volumes is similar to the one described for 2D, using pairs of input and fully annotated label volumes as training data.


This package requires Python 3.5 (or newer).

Please first install TensorFlow 1.x by following the official instructions. (Do not choose a preview release version of TensorFlow 2.x) 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.

StarDist can then be installed with pip:

pip install stardist


  • Depending on your Python installation, you may need to use pip3 instead of pip.
  • Since this package relies on a C++ extension, you could run into compilation problems (see Troubleshooting below). We currently do not provide pre-compiled binaries.
  • StarDist uses the deep learning library Keras, which requires a suitable backend (we currently only support TensorFlow).
  • (Optional) You need to install gputools if you want to use OpenCL-based computations on the GPU to speed up training.
  • (Optional) You might experience improved performance during training if you additionally install the Multi-Label Anisotropic 3D Euclidean Distance Transform (MLAEDT-3D).


We provide example workflows for 2D and 3D via Jupyter notebooks that illustrate how this package can be used.


Installation requires Python 3.5 (or newer) and a working C++ compiler. We have only tested GCC (macOS, Linux), Clang (macOS), and Visual Studio (Windows 10). Please open an issue if you have problems that are not resolved by the information below.

If available, the C++ code will make use of OpenMP to exploit multiple CPU cores for substantially reduced runtime on modern CPUs. This can be important to prevent slow model training.


Although Apple provides the Clang C/C++ compiler via Xcode, it does not come with OpenMP support. Hence, we suggest to install the OpenMP-enabled GCC compiler, e.g. via Homebrew with brew install gcc. After that, you can install the package like this (adjust names/paths as necessary):

CC=/usr/local/bin/gcc-8 CXX=/usr/local/bin/g++-8 pip install stardist


Please install the Build Tools for Visual Studio 2019 from Microsoft to compile extensions for Python 3.5 and newer (see this for further information). During installation, make sure to select the C++ build tools. Note that the compiler comes with OpenMP support.

How to cite

  author    = {Uwe Schmidt and Martin Weigert and Coleman Broaddus and Gene Myers},
  title     = {Cell Detection with Star-Convex Polygons},
  booktitle = {Medical Image Computing and Computer Assisted Intervention - {MICCAI} 
  2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part {II}},
  pages     = {265--273},
  year      = {2018},
  doi       = {10.1007/978-3-030-00934-2_30}

  author    = {Martin Weigert and Uwe Schmidt and Robert Haase and Ko Sugawara and Gene Myers},
  title     = {Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy},
  journal   = {arXiv:1908.03636},
  year      = {2019}

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