Understanding the mechanisms of cell motility and their regulation is a relevant challenge in biomedical research . The ability of cells to exert forces on their environment and alter their shape as they move  is essential to various biological processes such as the immune response , embryonic development , or tumorigenesis . This has been traditionally done in two dimensions using phase enhancing microscopy techniques such as Phase Contrast or Differential Interference Contrast microscopy. Recent technological advances in three-dimensional (confocal, multiphoton, SPIM) fluorescence microscopy have given researchers the opportunity to examine these processes in three dimensions within a living organism or in culture . This requires accurately detecting the cells (Segmentation) and their changes (Tracking) as they move through the environment. This includes not only traction-related changes, but also mitotic and apoptotic events that affect the cell lineages.
Manually segmenting and tracking cells is an extremely laborious task, due to the large amount of image data acquired during live-cell studies. Thus, the analysis of time-lapse experiments increasingly relies on automated image processing techniques. Most of the standard segmentation and tracking techniques do not perform well under the low-quality conditions (high cell density, inhomogeneous staining, lineage changes) typical of time-lapse video sequences  added to the nonlinear, differential response of the optical system at different depths of thick samples . This has encouraged the development of a significant number of algorithms that overcome these problems.
The state of the art of the existing cell segmentation algorithms range from simple thresholding methods [9,10], hysteresis thresholding , edge detection , or shape matching [13,14], to more sophisticated approaches based on region growing [15-17], machine learning [18-21] or energy minimization approaches [22-29]. See two reviews for a more comprehensive analysis of the existing cell segmentation methods and their antecedents [30,31].
The tracking methods can broadly be classified as tracking by detection [31-35] or tracking by model evolution [24-28,37-40] methods. The key idea of the former is to detect first all cells in the entire time-lapse video sequences and then associate the detected cells between successive frames, typically by optimizing a probabilistic objective function. The principle of the latter involves finding cells in the first frame and updating their position, shape, and orientation through the entire time-lapse series by taking the result from the previous frame into consideration. Each cell to be tracked is represented by a model that is evolved in time to fit the particular cell in the subsequent frames. Active contours, based on either an explicit [41-43] or implicit formulation [44-47], have been the first choice for the tracking by model evolution approach for many years.
Nowadays, the prevailing approach to both segmentation and tracking is to apply machine learning techniques [48-49].
Cell Tracking Challenge History
In 2012 we launched the Cell Tracking Challenge, with the aim of fostering the development of novel, robust cell tracking algorithms, and to help the developers with the evaluation of their new developments. In the first Cell Tracking Challenge, organized under the auspices of ISBI 2013 in San Francisco (CA, USA), we called for submission of algorithms developed under any of the two tracking frameworks described above. Over sixty groups registered for the challenge, of which six groups finally submitted consistent results that were evaluated and compared in terms of accuracy –both shape segmentation and lineage tracking-, and time, as they analyzed sequences of fluorescently labeled cells and nuclei moving in 2D and 3D environments, both real and computer generated. Given the varying nature of the data, 2D, 3D, including either nuclei or whole cells, the evaluation was done separately for each data type. Therefore, participants were allowed to submit more than one algorithm addressing the specific problems of different datasets. A report covering the logistics, methods and results of the challenge was published in Bioinformatics .
Given the success of the first edition of the challenge, measured in the number of registered participants and attendees to the workshop held during the ISBI 2013 conference, a second Cell Tracking Challenge was organized under the auspices of ISBI 2014. To broaden the scope of the challenge and increase the interest from potential participants, new datasets were added to the ones used in the first edition. Namely, 3D developmental fluorescence microscopy data, along with 2D, phase contrast and differential interference contrast (DIC) microscopy data were added to the existing ones. We also extended the simulated data using new sequences produced with a more sophisticated version of our existing cell simulator . Over sixty participants registered for the challenge, and eight of them submitted consistent results, that were evaluated, presented at the ISBI 2014 conference in Beijing (China) and posted on the challenge website. A thorough evaluation of the tracking performance measure used in this and future editions of the challenge was published in PLoS One .
In the third edition, held under the auspices of the ISBI 2015 conference in Brooklyn (NY, USA) the challenge was consolidated by the increased number of participants and submissions, especially for the most challenging datasets. In addition, given the growing relevance of high-throughput large-scale embryonic developmental data, we added a new dataset consisting of Drosophila melanogaster embryonic data imaged using light-sheet microscopy, to foster the development of automated tools for these extremely challenging datasets [53-54].
A comprehensive description of three editions of the challenge along with the outstanding results obtained at that time was published in Nature Methods in 2017 .
In October 2018 we launched the fourth fixed-deadline edition of the challenge with a new segmentation-centric benchmark, evaluating detection and segmentation results submitted for the existing and new challenge datasets. This new benchmark had a fixed initial deadline and was held under the auspices of ISBI 2019. The new challenge datasets consisted of two new types of image data, namely 3D cartographic projections of Tribolium Castaneum embryonic data imaged using light-sheet microscopy and single cells (both real and simulated) with filopodial protrusions that are difficult to segment. Thirteen participants submitted consistent results that were evaluated, presented at the ISBI 2019 conference in Venice (Italy), and posted on the challenge website.
Running Challenge Benchmarks
Since February 21st, 2017, the Cell Tracking Benchmark has remained open for online submissions that are evaluated periodically on a monthly basis. Since April 1st, 2019, the Cell Segmentation Benchmark has been opened for online submissions that are evaluated with the same frequency. The leaderboards containing top-3 performing methods for both the Cell Tracking Benchmark and the Cell Segmentation Benchmark are continually updated. Since January 8th, 2020, the silver reference segmentation annotations have been available for nine datasets to further facilitate tuning of competing algorithms, providing denser sets of cell segmentation masks compared to the previously existing gold reference segmentation annotations.
Challenge Related Studies
The gathered pool of annotated datasets along with the gathered pool of segmentation and tracking methods constitutes a unique resource for further studies and analyses. In addition to our own research, we are open to common collaborative projects led by other groups with the involvement of selected Cell Tracking Challenge organizers who can perform evaluations using the non-public gold reference annotations of the challenge datasets. In case of interest, please consult your intention with one of the Steering Committee members who will then discuss the feasibility of your proposal and available human and/or technical resources on our side with the rest of the Steering Committee.
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We gratefully acknowledge the support of the NVIDIA Corporation and their donation of the Quadro P6000 GPU used for the evaluation of challenge results. We also thank the IT4Innovations National Supercomputing Center for hosting the computations of silver reference segmentation annotations on their cluster.