A general route to volumetric segmentation under annotation scarcity
In the authors' words
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.
Main resultThe abstract does not state a limitation.
Appeared: Thursday, September 24. bioRxiv. Preprint, not yet peer-reviewed.