Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation

Kazuya Nishimura, Junya Hayashida, Chenyang Wang, Dai Fei Elmer Ker, Ryoma Bise ;


We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of ""cell detection"" (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train a co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that the co-detection CNN implicitly learns association in addition to detection. To obtain the association information, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the detection maps output of the co-detection CNN. Experiments demonstrated that the proposed method can match positions by analyzing the co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method."

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