StarDist

Models objects as star-convex polygons or polyhedra, providing a balance between flexibility and regularity that excels at segmenting irregular cells in dense environments, as demonstrated in the MICCAI and WACV papers.

Handles both 2D images and 3D volumetric data, making it suitable for a wide range of microscopy techniques, from fluorescence to histopathology, with dedicated workflows in the example notebooks.

Includes out-of-the-box models for common tasks like nuclei segmentation in fluorescence and H&E images, allowing quick application without training, as listed under 'Pretrained Models for 2D'.

Supports classification of detected instances into discrete classes, enabling cell type differentiation alongside segmentation, as shown in the ISBIC paper and multi-class example notebook.