![]() ![]() Moreover, if a cell changes morphology between subsequent timeframes, it can be seen as two different cells, yielding broken trajectories. Furthermore, feature matching algorithms rely on good segmentation to keep the match as accurate as possible. Typically, these techniques require the user to specify the maximal distance that cells can travel between two consecutive timeframes. This method locates similar cells using an extended list of features such as morphology, volume, surface, and total curvature that expand the concept of distance beyond spatial location. In other cases, a different approach termed feature matching gives better results. This option works well if the cells are moving slowly with respect to the chosen frame-rate and are not densely packed. Here, ‘nearest’ may refer to the spatial location, and cell centroids can thus be used as reference points. The easiest way to accomplish this association task is to connect every segmented cell in a frame to the nearest cell in the subsequent frame. Once cell positions have been identified, they need to be connected over time to form cell trajectories. For example, a 2D particle-based tracking algorithm has been extended to 3D taking into account adjacent optical planes, thus inferring the cell motion along the z-axis [ Given these computational challenges, few methods currently exist for automated 3D cell tracking, of which most have been developed to work with fluorescent microscopy. However, this requires further computations to connect the 2D regions-of-interest along the z-axis. In 3D image processing, segmentation can be directly performed at each xy-plane, and then extended to the third dimension. In addition, the resolution of most confocal microscopy techniques in the xy-plane is at least threefold better than in the z-axis, requiring special adjustments to be made, such as image interpolation or anisotropic filtering. A major issue is the increased computational cost of the corresponding algorithms. The analysis of complex 3D microscopy datasets brings many specific computational challenges in key processing tasks such as image registration, cell segmentation, and cell tracking. In the past 20 years, research strategies have therefore increasingly shifted towards 3D time-lapse (3D+t or, sometimes referred to as 4D in literature) imaging of migrating cells, both in vitro and in vivo. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. Cell migration is central to the development and maintenance of multicellular organisms. ![]()
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