  We study the problem of instance segmentation in biological images with
crowded and compact cells. We formulate this task as an integer program where
variables correspond to cells and constraints enforce that cells do not
overlap. To solve this integer program, we propose a column generation
formulation where the pricing program is solved via exact optimization of very
small scale integer programs. Column generation is tightened using odd set
inequalities which fit elegantly into pricing problem optimization. Our column
generation approach achieves fast stable anytime inference for our instance
segmentation problems. We demonstrate on three distinct light microscopy
datasets, with several hundred cells each, that our proposed algorithm rapidly
achieves or exceeds state of the art accuracy.
