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Star-convex shapes arise across bio-microscopy and radiology in the form ofnuclei, nodules, metastases, and other units. Existing instance segmentationnetworks for such structures train on densely labeled instances for eachdataset, which requires substantial and often impractical manual annotationeffort. Further, significant reengineering or finetuning is needed whenpresented with new datasets and imaging modalities due to changes in contrast,shape, orientation, resolution, and density. We present AnyStar, adomain-randomized generative model that simulates synthetic training data ofblob-like objects with randomized appearance, environments, and imaging physicsto train general-purpose star-convex instance segmentation networks. As aresult, networks trained using our generative model do not require annotatedimages from unseen datasets. A single network trained on our synthesized dataaccurately 3D segments C. elegans and P. dumerilii nuclei in fluorescencemicroscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM,and placental cotyledons in human fetal MRI, all without any retraining,finetuning, transfer learning, or domain adaptation. Code is available athttps://github.com/neel-dey/AnyStar.

AnyStar: Domain randomized universal star-convex 3D instance segmentation