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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automated mitotic detection in time-lapse phasecontrast microscopy providesus much information for cell behavior analysis, and thus several mitosisdetection methods have been proposed. However, these methods still have twoproblems; 1) they cannot detect multiple mitosis events when there are closelyplaced. 2) they do not consider the annotation gaps, which may occur since theappearances of mitosis cells are very similar before and after the annotatedframe. In this paper, we propose a novel mitosis detection method that candetect multiple mitosis events in a candidate sequence and mitigate the humanannotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In thistraining, the loss gradually decreases with the gap size between ground truthand estimation. This mitigates the annotation gaps. Our method outperformed thecompared methods in terms of F1- score using a challenging dataset thatcontains the data under four different conditions.

Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN