Abstract
Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method.
Highlights
Cellular dynamics are important with respect to many biological processes that directly affect human health [1]
Cell tracking is an effective method for quantitative analysis of cell migration characteristics, this requires a lot of effort and cost
We present an effective cell segmentation and tracking method for phase-contrast microscopic images
Summary
Cellular dynamics are important with respect to many biological processes that directly affect human health [1]. In the case of immune cells, studies on the effectiveness of antigen targeting and the individual motility of cells to enhance the effectiveness are being conducted [8] These eukaryotic cells have different motor forms and properties, and they are affected by different types of extracellular matrixes, the types of feet that the cells use to exercise, such as the pseudopodium, characterize their basic motility [9]. The study of these cell migration characteristics is generally conducted through probabilistic model analyses, such as persistent random walk and levy walk, by tracking the positions of cells at each interval in the images of the observed cells after several hours of taking a picture with a real-time microscope [7,10]. Many studies have been conducted on automatic cell tracking methods [11,12,13]
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