Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
This method is significant for microscopic experiments aiming to understand molecular and cellular function, particularly when dealing with dynamically localized proteins that cause temporary signal loss for segmentation.
This paper addresses the challenge of cell segmentation when the structures of interest are only temporarily visible in image sequences. The authors integrate uncertainty estimation into Mask R-CNN and propagate motion-corrected segmentation masks from frames with low uncertainty to those with high uncertainty, outperforming frame-by-frame segmentation and regular temporal propagation on HEK293T cell data.
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.