BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks. (arXiv:2206.04558v1 [cs.CV])

Despite its broad availability, volumetric information acquisition from
Bright-Field Microscopy (BFM) is inherently difficult due to the projective
nature of the acquisition process. We investigate the prediction of 3D cell
instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly
supervised method for volumetric instance segmentation of cells which only
requires approximate cell centroids annotation. Created pseudo-labels are
thereby refined with a novel refinement loss with Z-stack guidance. The
evaluations show that our approach can generalize …

arxiv cv segmentation stacks