Stain-free, single-cell segmentation and tracking is tantamount to the holy grail of microscopic cell migration analysis. Phase contrast microscopy (PCM) images with cells at high density are notoriously difficult to segment accurately; thus, manual segmentation remains the de facto standard practice. In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. Stain-free, instance-aware segmentation is accomplished using a mask regional convolutional neural network (Mask R-CNN). A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. Usiigaci provides highly accurate cell movement and morphological information for quantitative cell migration analysis.Cell migration is a fundamental cell behavior that underlies various phys-2 iological processes, including development, tissue maintenance, immunity, 3 and tissue regeneration, as well as pathological processes such as metastasis.
4Many in vitro as well as in vivo platforms have been developed to investigate 5 molecular mechanisms underlying cell migration in different microenviron-6 ments with the aid of microscopy. To analyze single-or collective-cell migra-7 tion, reliable segmentation of each individual cell in microscopic images is 8 necessary in order to extract location as well as morphological information.
9Among bright-field microscopy techniques, Zernike's phase contrast mi-10 croscopy (PCM) is favored by biologists for its ability to translate phase dif-11 ferences from cellular components into amplitude differences, so as to make 12 the cell membrane, the nucleus, and vacuoles more visible [1]. However,
13PCM images are notoriously difficult to segment correctly using conven-14 tional computer vision methods, due to the low contrast between cells and 15 their background [2]. For this reason, many cell migration experiments still 16 rely on fluorescent labeling of cells or manual tracking. Fluorescent labeling 17 of cells requires transgenic expression of fluorescent proteins or cells tagged 18 with fluorescent compounds, both of which can be toxic to cells and which 19 require extensive validation of phenotypic changes. Although thresholding 20 fluorescent images is relatively straightforward, cells that are in close prox-21 imity are often indistinguishable in threshold results. On the other hand, 22 manual tracking of cell migration is labor-intensive and prone to operator 23 error. Conducting high-throughput microscopy experiments is already possi-24 ble thanks to methodology and instrumental advances, but current analytical 25 techniques to interpret results quantitatively face major obstacles due to im-26 perfect cell segmentation and tracking [3]. Moreover, cell movement is not 27 the only parameter of interest in cell migration. For cell migration guided 28 by environmental gradients, shear stress, surface topolog...
