StarDist is a deep learning-based method for 2D and 3D nucleus detection, developed & published by Martin Weigert and Uwe Schmidt: github.com/stardist/. StarDist uses a cell detection method that predicts a shape representation with star-convex polygons that is well-suited to approximate the typically roundish shapes of cell nuclei in microscopy images.The 3D shape of a single object (cell nucleus) is described using a star-convex polyhedron instead of polygons.

StarDist is reported to run well in multiple open source environments such as Fiji/ImageJ, Qupath or Python (Using Python editor like Jupyter Notebook, PyCham and Spider), but what are the benefits of integrating StarDist directly inside of your arivis Vision4D imaging software? In this document, we will highlight the advantages of integrating open source analysis tools such as StarDist directly in Vision4D.