English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Deploying off-the-shelf segmentation networks on biomedical data has becomecommon practice, yet if structures of interest in an image sequence are visibleonly temporarily, existing frame-by-frame methods fail. In this paper, weprovide a solution to segmentation of imperfect data through time based ontemporal propagation and uncertainty estimation. We integrate uncertaintyestimation into Mask R-CNN network and propagate motion-corrected segmentationmasks from frames with low uncertainty to those frames with high uncertainty tohandle temporary loss of signal for segmentation. We demonstrate the value ofthis approach over frame-by-frame segmentation and regular temporal propagationon data from human embryonic kidney (HEK293T) cells transiently transfectedwith a fluorescent protein that moves in and out of the nucleus over time. Themethod presented here will empower microscopic experiments aimed atunderstanding molecular and cellular function.

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