We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell tracking algorithms. With twenty-one participating algorithms and a data repository consisting of thirteen datasets of various microscopy modalities, the challenge displays today’s state of the art in the field. We analyze the results using performance measures for segmentation and tracking that rank all participating methods. We also analyze the performance of all algorithms in terms of biological measures and their practical usability. Even though some methods score high in all technical aspects, not a single one obtains fully correct solutions. We show that methods that either take prior information into account using learning strategies or analyze cells in a global spatio-temporal video context perform better than other methods under the segmentation and tracking scenarios included in the challenge.

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.Contact: codesolorzano@unav.esSupplementary information: Supplementary data are available at Bioinformatics online.

Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.

Polycomb group (PcG) proteins, organized into Polycomb bodies, are important regulatory components of epigenetic processes involved in the heritable transcriptional repression of target genes. Here, we asked whether acetylation can influence the nuclear arrangement and function of the BMI1 protein, a core component of the Polycomb group complex, PRC1. We used time-lapse confocal microscopy, micro-irradiation by UV laser (355 nm) and GFP technology to study the dynamics and function of the BMI1 protein. We observed that BMI1 was recruited to UV-damaged chromatin simultaneously with decreased lysine acetylation, followed by the recruitment of heterochromatin protein HP1β to micro-irradiated regions. Pronounced recruitment of BMI1 was rapid, with half-time τ = 15 sec; thus, BMI1 is likely involved in the initiation step leading to the recognition of UV-damaged sites. Histone hyperacetylation, stimulated by HDAC inhibitor TSA, suppression of transcription by actinomycin D, and ATP-depletion prevented increased accumulation of BMI1 to γH2AX-positive irradiated chromatin. Moreover, BMI1 had slight ability to recognize spontaneously occurring DNA breaks caused by other pathophysiological processes. Taken together, our data indicate that the dynamics of recognition of UV-damaged chromatin, and the nuclear arrangement of BMI1 protein can be influenced by acetylation and occur as an early event prior to the recruitment of HPβ to UV-irradiated chromatin.