  Deploying off-the-shelf segmentation networks on biomedical data has become
common practice, yet if structures of interest in an image sequence are visible
only temporarily, existing frame-by-frame methods fail. In this paper, we
provide a solution to segmentation of imperfect data through time based on
temporal propagation and uncertainty estimation. We integrate uncertainty
estimation into Mask R-CNN network and propagate motion-corrected segmentation
masks from frames with low uncertainty to those frames with high uncertainty to
handle temporary loss of signal for segmentation. We demonstrate the value of
this approach over frame-by-frame segmentation and regular temporal propagation
on data from human embryonic kidney (HEK293T) cells transiently transfected
with a fluorescent protein that moves in and out of the nucleus over time. The
method presented here will empower microscopic experiments aimed at
understanding molecular and cellular function.
