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/.

Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes that create top-down hierarchical tree are a useful platform for developing optimization algorithms to determine the 'optimal' clusters from a pairwise proximity matrix which represents completely connected and weighted graph. Evolution strategy is applied to determine the 'globally optimal' incisional hyperplanes to construct hierarchical tree structure and tested with Fisher's iris and Golub's leukemia data sets. The results were compared with those of bottom-up hierarchical clustering, K-means and SOMs (Self-Organizing Maps) algorithms with promising results.