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
this https URL

提出了一种基于深度学习的细胞轮廓跟踪方法，使用无监督学习实现了机械和周期性一致性损失函数来训练轮廓跟踪器，对相位对比和共聚焦荧光显微镜拍摄的两个数据集进行了稀疏跟踪点标记以得到定量评估结果，其轮廓跟踪器比其他方法表现更好且产生更优质的结果。