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Funding: This study was funded, in part, by ChyronHego and Deutsche Fußball Liga DFL. The funding sources were not involved in the study design, analysis and interpretation of the data, the writing of the manuscript or the decision to submit this article for publication. The Technical University of Munich, represented by the Chair of Training Science and Sports Informatics, functioned as an independent third party for conducting this validation study. The terms of this arrangement have been reviewed and approved by the Technical University of Munich in accordance with its policy on objectivity in research. This work was supported by the German Research Foundation (DFG) and the Technical University of Munich within the funding program Open Access Publishing.
Competing interests: This research was supported by a research fund that was received from ChyronHego and Deutsche Fußball Liga DFL. The Technical University of Munich, represented by the Chair of Training Science and Sports Informatics, functioned as an independent third party for conducting this validation study. The funders did not exhibit influence on study design, data collection and analysis, decision to publish, or preparation of the manuscript. These conditions do not alter our adherence to PLOS ONE policies on sharing data and materials. We declare that with ChyronHego and Deutsche Fußball Liga DFL, no relationship like employment, consultancy, patents, products in development, marketed products, etc. exists. Further, the authors do not own sticks or shares of STATS LLC and the authors are not paid for employment of consultancy. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Consequently, companies such as STATS (Chicago, US), Second Spectrum (Los Angeles, US), ChyronHego (New York, US), and Deltatre (Torino, Italy) have all provided video tracking systems to the market which allow positional data to be collected and used for live and post-match analysis, talent scouting, and media enhancement . A comprehensive survey of the state-of-the-art video-based player tracking systems can be found in Manafifard, Ebadi , and a survey on football video analysis was provided by Oskouie, Alipour . Worth mentioning in this context is that a variety of different video tracking systems exist, which can be broadly classified according to the number, arrangement, and specification of utilised cameras (single vs multiple, stationary vs dynamic, stereo vs monocular) .
Table 1 summarizes the number of trials, recording time, and recorded data points (frames) included for analysis. VICON trials including data gaps >1 s were excluded from the analyses. This resulted in the exclusion of 5 SSC trials and 5 SSG trials from analysis, which resulted in a total number of 90 individual trials (a trial is defined as single observation of an individual player during an exercise).
Estimating positional accuracy requires datasets to be precisely aligned. Therefore, datasets from VICON and TRACAB had to be aligned temporally and spatially. To achieve this, the original Vicon datasets were down-sampled from 100 Hz to 25 Hz to allow for comparisons with the TRACAB systems. The temporal alignment was then achieved by minimising the RMSE between the TRACAB and VICON signal, a method that provides a quantitative assessment of the similarity of the two signals at all possible time shifts, or time lags. The spatial alignment was achieved by means of a generalized Procrustes analysis (Euclidean similarity transformation, i.e. translation and rotation).
To determine a reasonable cut-off frequency, we applied the method proposed by Winter , which essentially consists of performing a residual analysis to examine the differences between the raw and filtered position data over a pre-set range of cut-off frequencies (from 0.1 to 10 Hz in steps of 0.1 Hz). For both TRACAB and VICON, this method resulted in an optimal cut-off frequency between 1 and 1.5 Hz. For the purpose of this study, we took the conservative choice of rounding down and using a 1 Hz cut-off frequency on the raw position data. A visualisation of the raw and smoothed position data is illustrated in Fig 5.
While not creating a general, flexible software tool, many groups have benefited from automated cell image analysis by developing their own scripts, macros, and plug-ins to accomplish specific image analysis tasks. Custom programs written in commercial software (for example, MetaMorph, ImagePro Plus, MATLAB) or Java have been used to identify, measure, and track cells in images and time lapse movies [10, 22, 23]. Such studies clearly show the power of automated image analysis for biological discovery. However, most of these custom programs are not modular, so combining several steps and changing settings requires interacting directly with the code and is simply not practical for routinely processing hundreds of thousands of images or sending jobs to a cluster. The effort expended by laboratories in creating an analysis solution with a particular software package is often lost after the initial experiment is completed; other laboratories rarely use the methods because they are customized for a particular cell type, assay or even image set. Furthermore, although developing a routine for a new cell type or assay usually requires testing multiple algorithms, it is impractical to implement and test several published methods for a particular project.
Furthermore, key challenges remain in image analysis algorithm development itself . Cell image analysis has been described as one of the greatest remaining challenges in screening [5, 29], and as a field is "very much in its infancy"  and "lag [s] behind the adoption of high-throughput imaging technologies" . Accurate cell identification is required to extract meaningful measures from images, but even for mammalian cell types, existing software often fails on crowded cell samples, which has severely limited screens thus far. Screens in most non-mammalian organisms have been limited to visual inspection.
In summary, while existing software enables particular assays for particular cell types, high throughput image analysis has, to this point, been impractical unless an image analysis expert develops a customized solution, or unless commercial packages are used with their built-in algorithms for a limited set of cellular features and for a limited set of cell types. There exists a clear need for a powerful, flexible, open-source platform for high-throughput cell image analysis.
CellProfiler is freely available modular image analysis software that is capable of handling hundreds of thousands of images. The software contains already-developed methods for many cell types and assays and is also an open-source, flexible platform for the sharing, testing, and development of new methods by image analysis experts. CellProfiler meets the needs discussed in the introduction, in that it contains: advanced algorithms for image analysis that are able to accurately identify crowded cells and non-mammalian cell types; a modular, flexible design allowing analysis of new assays and phenotypes; open-source code so the underlying methodology is known and can be modified or improved by others; a user-friendly interface; the capability to make use of clusters of computers when available; and a design that eliminates the tedium of the many steps typically involved in image analysis, many of which are not easily transferable from one project to another (for example, image formatting, combining several image analysis steps, or repeating the analysis with slightly different parameters). CellProfiler was designed and optimized for the most common high-content screening image format, that is, two-dimensional images. It has very limited support for time-lapse and three-dimensional image stack analysis, although researchers interested in these areas could build compatible modules.
Most image analysis projects, even for new cell types or assays, can be accomplished simply by pointing and clicking using CellProfiler's graphical user interface (Figure 1a). The software uses the concept of a 'pipeline' of individual modules (Figure 1b; Additional data file 2). Each module processes the images in some manner, and the modules are placed in sequential order to create a pipeline: usually image processing, then object identification, then measurement. Over 50 CellProfiler modules are currently available (Additional data file 3). Most modules are automatic, but the software also allows interactive modules (for example, the user clicks to outline a region of interest in each image). Modules are mixed and matched for a specific project and each module's settings are adjusted appropriately. Upon starting the analysis, each image (or group of images if multiple wavelengths are available) travels through the pipeline and is processed by each module in order.
One of the most critical steps in image analysis is illumination correction. Illumination often varies more than 1.5-fold across the field of view, even when using fiber optic light sources, and occasionally even when images are thought to be already illumination-corrected by commercial image analysis software packages (TRJ, AEC, DMS, and PG, unpublished data). This adds an unacceptable level of noise, obscures real quantitative differences, and prevents many types of biological experiments that rely on accurate fluorescence intensity measurements (for example, DNA content of a nucleus, which only varies by two-fold during the cell cycle). CellProfiler contains standard methods plus our new methods  to address illumination variation, allowing various methods to be compared side by side and, ultimately, providing less noisy quantitative measures (Figure 1c,d). We use these illumination correction methods for every high-throughput image set we process, because using raw images degrades intensity measurements and, less obviously, can preclude accurate cell identification. This adversely affects all types of measurements, from intensity based measures (for example, DNA content histograms ) to area and shape measurements (TRJ, AEC, DMS, and PG, unpublished data). CellProfiler's other image processing modules perform other needed adjustments prior to identifying cells in images, for example, aligning or cropping (Additional data file 3). 2b1af7f3a8