Reliable analysis of adult stem cell populations in in vitro experiments still poses a problem on the way to fully understand the regulating mechanism of these cultures. However, it is essential in the use of cultivated endogenous cells in stem cell therapies. One crucial feature during automated analysis is clearly the robust detection of mitotic events. In this work, we use the fully labeled stem cell benchmark data set CeTReS I in order to evaluate different approaches of mitosis detection: a purely time line based approach; a feature-based motility detector; and a detector based on the cell morphology changes, for which we also propose an adaptive version. We demonstrate that the approach based on morphological change outperforms the static detectors. However, the set of optimal features is changing over time, and thus it is not surprising that a feature set adapted to the systems confluency shows the best performance.
In order to fully understand the behavior and regulating mechanisms of in vitro
stem cell populations, time-lapse phase contrast microscopy plays a major role.
This technique enables the observation and analysis of a cell population in a
completely label-free and noninvasive manner and allows a simultaneous
processing of thousands of cells in vitro. An automated cell detection and tracking
system facilitates the analysis of these data and represents a big improvement
to the actual gold standard method: the tedious work of manual cell tracking.
The key event in analyzing this data is cell division or mitosis. The mitosis is
the most difficult part to detect as it implies a strong non-linearity or, in other
words, new cells and tracking candidates are added to the scenario. However, an
accurate knowledge of these events enables the reconstruction of cell lineages [
All detection and tracking tasks were performed using the freely available data
set CeTReS I [
Within this study, we use a straightforward algorithm to detect the cells
automatically on the given microscope images. To get a consistent input, all images
were preprocessed in two steps: first, an image normalization was used to expand
the dynamic range of the data; second, the contrast was increased by performing
a histogram equalization. To perform cell detection, each image is segmented
using the Otsu intensity threshold. The result of this operation is a binary
image that is further refined by morphological filters. This algorithm achieves a
false acceptance rate (FAR) and false rejection rate (FRR) of 5% for cell
detection and thus give a detection quality comparable with the state-of-the-art
approaches [
In general, there are two main approaches to detect mitoses: first, through
temporal context; second, through a spatio-temporal pattern. Using the
temporal context, a mitosis can be found as one cell (mother) divides into two cells
(daughters). In a binary tree, this gives the typical Y-shape in a family tree.
Furthermore, mitotic cells feature a characteristic spatio-temporal pattern
(illustrated in Fig. 2). In particular, they contract, become smaller, rounder, appear
brighter and decrease their velocity. Based on these criteria, four detectors were
implemented for comparison:
{ Y detector: The naive approach classifies each path p as mitotic if it can be
linked to two successive cell paths and both have p as their solely predecessor.
{ Motility detector The second detector is motivated by Debeir et al. [
Prob(d) =
1 √(2 )4det( ) exp ( (d 1 (d
) ) )T 2 (1) where denotes the covariance matrix and the mean feature difference vector. Values for and are trained using the reference data. These features combine to a set of potential classifiers c(!; d), with ! 2 f0; 1gN indicating which of the N features are turned on or off. { Adaptive morphology detector Finally, we propose a detector that substitutes ! in c(!; d) by a time-dependent feature parameter !(t). This appears to be a reasonable extension as single cell morphology is strongly changing over time due to population effects, e.g., confluency. 3
The reference data CeTReS I was used to evaluate the proposed detectors. This set contains a total of 4689 cell paths and 2019 labeled mitotic events, of which 1903 (95%) are successfully found by our proposed detection and tracking algorithm (2.2). To compare the different detection results, the false acceptance rate (FAR=specificity) and false rejection rate (FRR=sensitivity) for each detector was calculated with varying thresholds. In Figure 3(a), the ROC (Receiver Operator Characteristic) curves are shown for the different classifiers. The motility detection clearly performs worst. The motility detector is outperformed by the morphology detector using only size as a feature. Note that the motility variant use cell size as a static measure while the morphology detector is working on size differences. As shown, the combination of brightness and size parameter resulted in the best performance for the morphology detector. Figure 3(b) illustrates the behavior of the different detection strategies over time. The motility detection shows a strong decrease in performance leading to the worst result. Some of the morphology features show a contrary behavior: size has a decreasing performance while compactness has an increasing performace over time. This illustrates why the adaptive detection strategy performs best. From the biological point of view, (a) ROC for different detections. (b) EER over time detection algorithm complete paths reliability the most important measure is the rate of correct detected complete paths, i.e. paths that describe complete cell cycles from the “birth” mitosis to the “end” mitosis. The results presented in table 1 show the number of detected paths and their reliability. To achieve the most reliable results, the Y detection was used to preselect possible mitosis candidates. 4
In this paper, we present a mitosis detection based on the morphological change
of the cells during division. The novelty of this algorithm lies in the fact that it
adapts to the changes of cell appearance during culturing. Therefore, it
outperforms other, non adaptive approaches like those of [