Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency

• URL: http://arxiv.org/abs/2107.08639v1
• Date: Mon, 19 Jul 2021 06:40:47 GMT
• Title: Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency
• Authors: Kazuya Nishimura and Hyeonwoo Cho and Ryoma Bise
• Abstract summary: 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.
• Score: 10.20554144865699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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