Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins

TL;DR

This paper introduces a method that enhances cell segmentation in biomedical image sequences by integrating uncertainty estimation into Mask R-CNN and propagating masks temporally to handle transient signal loss.

Contribution

The novel approach combines uncertainty-aware segmentation with temporal propagation to improve accuracy in dynamic cellular imaging data.

Findings

Outperforms frame-by-frame segmentation methods.

Effectively handles temporary signal loss in cell imaging.

Improves segmentation consistency over time.

Abstract

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.