Figure 2. Concept of the main factors that determine the quality of cell images and videos.
a–f. Signal to Noise Ratio (SNR) and Contrast Ratio (CR) measure the relationship between the signal captured from the cells and the unwanted noise or signal captured at the same time. Decreasing SNR is shown using a cell with 250 intensity units (iu) and no background (0 iu) in three scenarios of increasing standard deviation (std, in iu) of background Gaussian noise: 0 (a); 50 (b); 200 (c). The effect of decreased CR is displayed using a simulated cell in high background (200 iu) with increasing noise std: 0 (d); 50 (e); 200 (f). The effect is shown for three increasing noise: 0 noise (a vs. d); 50 noise std (b vs. e); 200 noise std (c vs. f). g–h. Intra-cellular signal heterogeneity that can lead to cell over-segmentation when the same cell yields several detections is simulated by a cell with non-uniform distribution of the labeling marker or non-label retaining structures (g). Signal texture can also be linked to the process of image formation, in this case shown using a simulated cell image imaged by Phase Contrast microscopy (h). i. Signal heterogeneity between cells, shown by simulated cells with different average intensities can be due, for instance, to different levels of protein transfection, non-uniform label uptake, or cell cycle stage or chromatin condensation, when using chromatin-labeling techniques. j–l. Spatial resolution that can compromise the accurate detection of cell boundaries is displayed using a cell captured with increasing pixel size, i.e., with decreasing spatial resolution: full resolution (j); half resolution (k); one fourth of the original full resolution (l). m–n. Irregular shape that can cause over/under-segmentation, especially when the segmentation methods assume simpler, non-touching objects, is displayed using a simulated cell with highly irregular shape under two background noise std situations: 0 (m); 100 (n).This is especially a problem in high-noise situations (n). o. High density of cells, also frequent cause of incorrect segmentation is shown by a cluster of simulated cells. p–r. Fluorescence temporal decay that can bring the SNR or CR below detection levels, thus complicating both segmentation and tracking, is simulated by a cell in a time series, showing increasing fluorescence decay due to bleaching or quenching of the fluorochrome, and same noise conditions (std of 50 iu): original cell at the beginning of the experiment (p); cell with 100 iu decay (q); cell with 200 iu decay (r). s–u. Cell overlap between consecutive frames is key for correctly tracking the cells since many algorithms rely on this overlap. Here it is shown using three simulated cells at the beginning of a video (t=0) (s) and two possible alternative scenarios for the following time point (t=1): t=1 in a scenario of high temporal resolution and/or low cell speed, allowing relatively simple identification of the correspondence between the cells (t); t=1 in a scenario of low temporal resolution and/or high cell speed, complicating the identification of the correspondence between the cells (u). v–x. Number and synchronization of mitoses also complicates cell tracking, since tracking a mitotic cell requires correctly assigning the mother to its daughter cells in consecutive frames. This is simulated by cells at the beginning of the video (t=0) (v) and two possible alternative scenarios for the following time point (t=1): t=1 in a scenario where only one of the cell divides asynchronously allowing a simple lineage assignment of mother and daughter cells (w); t=1 in a scenario of multiple, synchronized division events rendering a complicated lineage assignment of mothers and daughters (x).