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.

Image cytometry still faces the problem of the quality of cell image analysis results. Degradations caused by cell preparation, optics, and electronics considerably affect most 2D and 3D cell image data acquired using optical microscopy. That is why image processing algorithms applied to these data typically offer imprecise and unreliable results. As the ground truth for given image data is not available in most experiments, the outputs of different image analysis methods can be neither verified nor compared to each other. Some papers solve this problem partially with estimates of ground truth by experts in the field (biologists or physicians). However, in many cases, such a ground truth estimate is very subjective and strongly varies between different experts. To overcome these difficulties, we have created a toolbox that can generate 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The user can then apply image analysis methods to such simulated image data. The analysis results (such as segmentation or measurement results) can be compared with ground truth derived from input object digital phantoms (or measurements on them). In this way, image analysis methods can be compared with each other and their quality (based on the difference from ground truth) can be computed. We have also evaluated the plausibility of the synthetic images, measured by their similarity to real image data. We have tested several similarity criteria such as visual comparison, intensity histograms, central moments, frequency analysis, entropy, and 3D Haralick features. The results indicate a high degree of similarity between real and simulated image data. ' International Society for Advancement of CytometryKey terms digital phantom; synthetic image; procedural texture; point spread function; fluorescence optical microscope; 3D image cytometry CURRENT biomedical research relies strongly on computer-based evaluation, as the vast majority of commonly used acquisition techniques produce large numbers of numerical or visual data. Each individual technique (optical microscopy, PET, MRI, CT, ultrasound, etc. (1)) has its advantages that make it suitable for use in selected fields of research. However, each technique also has its own drawbacks (blur, noise, various aberrations) (2-4). In this sense, one should keep in mind that biomedical data supplied by scientific or diagnostic instruments always suffer from some imperfection and therefore cannot be directly used for further analysis, evaluation or measurement. To guarantee more accurate results, some preprocessing is required.Let us focus on the area of image reconstruction. Here, the search for ground truth approximation is called an image restoration (5-7), and it constitutes the best possible method for image recovery. It is done in time-reverse order; that is, the image defect caused by the phenomenon that appeared in the whole acquisition process as the first one must be eliminated as the last...

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The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.