Year: 2026
Type: preprint
Abstract: Volumetric cell segmentation is limited by the scarcity of labelled data, because manual curation of three-dimensional datasets is prohibitively laborious. We find that this gap can be bridged with volumes in which synthetic 3D objects mirror the properties of real cells and of image formation, notably the axial spread of the point-spread function. Such volumes provide unlimited labelled training data, and we show that a model trained on them produces accurate segmentations of real biological data.
Institution: University of Geneva
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214084270