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Analyzing the dynamic changes of cellular morphology is important forunderstanding the various functions and characteristics of live cells,including stem cells and metastatic cancer cells. To this end, we need to trackall points on the highly deformable cellular contour in every frame of livecell video. Local shapes and textures on the contour are not evident, and theirmotions are complex, often with expansion and contraction of local contourfeatures. The prior arts for optical flow or deep point set tracking areunsuited due to the fluidity of cells, and previous deep contour tracking doesnot consider point correspondence. We propose the first deep learning-basedtracking of cellular (or more generally viscoelastic materials) contours withpoint correspondence by fusing dense representation between two contours withcross attention. Since it is impractical to manually label dense trackingpoints on the contour, unsupervised learning comprised of the mechanical andcyclical consistency losses is proposed to train our contour tracker. Themechanical loss forcing the points to move perpendicular to the contoureffectively helps out. For quantitative evaluation, we labeled sparse trackingpoints along the contour of live cells from two live cell datasets taken withphase contrast and confocal fluorescence microscopes. Our contour trackerquantitatively outperforms compared methods and produces qualitatively morefavorable results. Our code and data are publicly available athttps://github.com/JunbongJang/contour-tracking/

Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses