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.

本研究提出一种弱监督细胞跟踪方法，该方法使用 “细胞检测” 注释（即细胞位置坐标）而不使用关联信息来训练卷积神经网络。该方法通过前向和后向传播分析了卷积神经网络检测图中细胞位置的对应关系，成功匹配了不同帧中的细胞位置，且性能接近于最新的监督方法。