论文标题
通过3DCNN估算的可能性图估计，空间 - 周期性有丝分裂检测
Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN
论文作者
论文摘要
延时术中的自动化有丝分裂检测为我们提供了许多细胞行为分析的信息，因此已经提出了几种有丝分裂检测方法。但是，这些方法仍然存在两个问题。 1）当紧密放置时，它们无法检测到多个有丝分裂事件。 2）他们不考虑注释差距，这可能会发生，因为有丝分裂细胞的出现在注释框架之前和之后非常相似。在本文中，我们提出了一种新颖的有丝分裂检测方法，该方法可以在候选序列中检测多个有丝分裂事件，并通过估计3DCNN估计时空可能性图来减轻人类注释差距。在这项培训中，随着地面真相和估计之间的间隙大小，损失逐渐减少。这减轻了注释差距。我们的方法使用具有挑战性的数据集在四个不同条件下包含数据的数据集优于F1分数的比较方法。
Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.
