Automated Tracking Turns Cell Culture into Numbers Figure 2. Cell detection and overlap tracking. Cells in the raw data (grey value pictures, A) are detected using a global threshold operation. The resulting binary representation is called cell mask (B). It contains all detected cells as connected, consecutively numbered pixel regions. Overlap tracking (C): Movement of a detected cell through different time points in an image stack (left). On the right, a graphical representation of the resulting overlap in adjacent images is given (dark areas in the overlap images); it is apparent that forward and backward overlap are numerically different because the cell area changes over time. doi:10.1371/journal.pone.0027315.g002 1 {ðd{mÞT S{1ðd{mÞ equation 1: Prob(d)~ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ e 2 2pN detðSÞ , where N denotes the size of the feature difference vector d, S denotes the covariance matrix and m the mean vector. Values for S and m are calculated using the reference data. This multi- factorial measure turned out to be a strong criterion for determining the identity of a cell in adjacent cell masks. In fact, it could be used to track cells independently of the overlap criterion as well as for a detailed description of individual cell behaviour. Mitosis detection. Mitoses are the single most important events for extracting genealogical nexus from time-lapse data, because they mark the regular start/end points of a cells life. A dedicated mitosis detector is therefore an essential tool for constructing correct genealogic trees and assessing the validity of a particular tracking result [20,26]. Here, we use two main criteria to identify mitoses: A path, entering mitosis must have a Y-shape, i.e. a mitotic cell has two daughter cells. Second, a mitosis displays a characteristic spatiotemporal pattern (figure 3), which can be searched for. In particular, a mitotic cell: * contracts and becomes smaller and rounder; * appears brighter in phase contrast. This pattern can be detected using the feature and difference vector f and d of each cell path. In contrast to the tracking problem, where the change of the feature vector needs to be smaller than a given threshold, it must be greater than a threshold for mitosis detection: a cell path is mitotic, if the probability Prob(d) from equation 1 exceeds a given threshold: Prob(d) . hM . The value for hM is estimated using the mitoses in the reference data set A. Path classification. Ideally, a path connects two mitoses, i.e. the whole life span of a cell. In reality, however, most paths start and end at less desirable points; paths can be classified according to the nature of these end points. These path classifications are necessary for later analysis (error classification, validation, statistics); they are stored in a flag-vector (see table 1). "Naive Tracking". To assess the improvement which is achieved with our novel validation method, we needed a very similar tracking approach, but without the validation part. Throughout this paper, we will call this unvalidated brute force tracking approach "naive tracking". It is accomplished through backward tracking by simply choosing the predecessor cell with the greatest overlap. If the naive assumption that all cells were correctly detected was true with a time resolution high enough, repeated use of this operation should in principle reconstruct the whole lineage of all cells. Figure 3. Typical spatiotemporal pattern of a mitosis in vitro. The spindle apparatus forces the cell to become spherical (i.e. the circularity rises) and partly detach from the growth surface (area decreases); also, the brightness rises temporarily as a consequence of the increased thickness. doi:10.1371/journal.pone.0027315.g003 PLoS ONE | www.plosone.org 4 November 2011 | Volume 6 | Issue 11 | e27315