Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/

翻译：分析细胞形态的动态变化对于理解活细胞（包括干细胞和转移性癌细胞）的各种功能与特征至关重要。为此，我们需要在活细胞视频的每一帧中追踪高度可变形细胞轮廓上的所有点。轮廓上的局部形状与纹理并不明显，其运动复杂，常伴随局部轮廓特征的扩张与收缩。由于细胞具有流动性，现有光流或深度点集追踪方法并不适用，而先前的深度轮廓追踪也未考虑点对应关系。我们首次提出了基于深度学习的细胞（更广义而言，粘弹性材料）轮廓点对应追踪方法，通过跨注意力机制融合两个轮廓之间的密集表征。由于手动标注轮廓上的密集追踪点不切实际，我们提出由力学和循环一致性损失构成的无监督学习来训练我们的轮廓追踪器。其中，强制点沿垂直于轮廓方向移动的力学损失有效提升了性能。为进行定量评估，我们使用相差显微镜与共聚焦荧光显微镜采集了两个活细胞数据集，并沿轮廓标注了稀疏追踪点。我们的轮廓追踪器在定量指标上优于对比方法，并生成了定性更优的结果。我们的代码与数据已公开于 https://github.com/JunbongJang/contour-tracking/