  Cell detection is the task of detecting the approximate positions of cell
centroids from microscopy images. Recently, convolutional neural network-based
approaches have achieved promising performance. However, these methods require
a certain amount of annotation for each imaging condition. This annotation is a
time-consuming and labor-intensive task. To overcome this problem, we propose a
semi-supervised cell-detection method that effectively uses a time-lapse
sequence with one labeled image and the other images unlabeled. First, we train
a cell-detection network with a one-labeled image and estimate the unlabeled
images with the trained network. We then select high-confidence positions from
the estimations by tracking the detected cells from the labeled frame to those
far from it. Next, we generate pseudo-labels from the tracking results and
train the network by using pseudo-labels. We evaluated our method for seven
conditions of public datasets, and we achieved the best results relative to
other semi-supervised methods. Our code is available at
https://github.com/naivete5656/SCDTC
