Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency
This addresses the time-consuming annotation problem for researchers in biomedical imaging, though it is incremental as it builds on existing semi-supervised and tracking techniques.
The paper tackles the problem of reducing annotation effort for cell detection in time-lapse microscopy images by proposing a semi-supervised method that uses one labeled image and temporal consistency to generate pseudo-labels, achieving the best results among semi-supervised methods on seven public dataset conditions.
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