propagate through time and, when the cells later depart from each other, may lead to false mitosis events and/or transposition of cells. Fortunately, merged cell errors can be identified swiftly through the reversed Y-shape in the path representation (two paths ending in a common successor). But, though the error is easy to detect, it can be hard to unwind, because the single cells loose their unique identity. One way to solve this problem would be to map in- and output cells of the merged-cell-scenario by comparing their feature vectors and choosing pairs with the smallest difference. However, such an algorithm must be used with caution, because it may introduce additional errors. Scenario 4: Debris. Sometimes, non-cell objects, such as cell debris or other particles will be falsely detected as cells; also we categorize dead cells as debris. These objects usually move in a different manner than adherently growing cells. They tend to drift rather fast and linearly (see fig. 4d). In our case, the fast movement causes the overlap tracking to fail; therefore, debris often manifests as paths, which appear suddenly ("lost begin"-flag set) and span one frame only. In our tracking experiments, debris did not appear in significant amounts (we counted four dead (detaching) cell events, two in each reference data set). In general, dead cells may pose a serious challenge to cell tracking when occurring more frequently than in the cell populations used here. Error classification / error decision tree. Figure 5 summarizes all error scenarios and assembles them in a complete decision tree to categorize possible tracking errors. The error decision tree puts all possible error scenarios in a hierarchy, such that it advances from simple scenarios, like cells at the image border, to less conclusive situations, like debris or vanishing cells. Validation algorithm, based on the error decision tree. Many errors can be detected using the path classification flags. First, all "border begin" and "border end" paths are removed from the output. Second, merged cell events and mitoses are specifically looked for through their unique connection patterns Automated Tracking Turns Cell Culture into Numbers ("merged" flag, "mitosis" flag). At this point, the ambiguous "lost end" and "lost begin" flags can only indicate a vanishing/re- appearing cell or cell debris/cell death. Vanished cells may become detectable with a local cell detector, e.g. using a local threshold instead of a global. Due to its unspecific nature, debris is the last candidate in the decision tree of error analysis (see figure 5). We have chosen to construct the validation algorithm such that after error classification/correction it returns complete paths only, i.e. whole cell lifes from mitosis to mitosis; path fragments are not accepted. Complete paths can be seen as the natural unit of validation, because most of the biological measures are based on statistical measures of whole cells (e.g. life time distributions, sibling symmetry, genealogic nexus). Although the correct classification of an error leads in most cases to its rectification, there are exceptions, when the validation process remains inconclusive even if all errors are correctly classified. This is particularly the case, if combinations of errors occur and multiple cells are involved. In such cases, either human intervention would be required or - in case of a fully automated validation - all involved paths must be removed from the tracking output. II) Reference data set Two different raw image stacks were analyzed thoroughly to construct the reference data set: The first (see Video S1) comprises 209 images, recorded every 15 minutes (i.e. 52 hours real time) and displays a total of 244.580 single cells. This reference data set will be analyzed throughout the paper; sample images at different time points are shown in figure 6. Set B (see Video S2, S3, S4, S5) consists of 399 images, taken at a time interval of 10 minutes (i.e. 66.5 hours real time) contains a total of only 80.500 single cells (cells were observed at lower densities). Set B was acquired in two different contrast modes (phase contrast and oblique illumination), which makes it particularly suitable for testing more specialised cell Figure 5. Complete classification of tracking errors. All possible tracking errors can be arranged in a decision tree; each decision/pink square belongs to an error scenario (details see text). If an error can be detected and classified, it can be corrected in most cases (green exclamation marks). Only, if all attempts to classify an error fail, the local tracking problem can be not solved by the validation algorithm and may require human intervention (red question marks). doi:10.1371/journal.pone.0027315.g005 PLoS ONE | www.plosone.org 6 November 2011 | Volume 6 | Issue 11 | e27315