  Automated mitotic detection in time-lapse phasecontrast microscopy provides
us much information for cell behavior analysis, and thus several mitosis
detection methods have been proposed. However, these methods still have two
problems; 1) they cannot detect multiple mitosis events when there are closely
placed. 2) they do not consider the annotation gaps, which may occur since the
appearances of mitosis cells are very similar before and after the annotated
frame. In this paper, we propose a novel mitosis detection method that can
detect multiple mitosis events in a candidate sequence and mitigate the human
annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this
training, the loss gradually decreases with the gap size between ground truth
and estimation. This mitigates the annotation gaps. Our method outperformed the
compared methods in terms of F1- score using a challenging dataset that
contains the data under four different conditions.
