Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Since manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized, for the first time, an open competition, in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to important practical conclusions for users and developers.

We introduce a new class of data-fitting energies that couple image segmentation with image restoration. These functionals model the image intensity using the statistical framework of generalized linear models. By duality, we establish an information-theoretic interpretation using Bregman divergences. We demonstrate how this formulation couples in a principled way image restoration tasks such as denoising, deblurring (deconvolution), and inpainting with segmentation. We present an alternating minimization algorithm to solve the resulting composite photometric/geometric inverse problem. We use Fisher scoring to solve the photometric problem and to provide asymptotic uncertainty estimates. We derive the shape gradient of our data-fitting energy and investigate convex relaxation for the geometric problem. We introduce a new alternating split-Bregman strategy to

We present a discrete, unsupervised multi-regioncompetition algorithm for image segmentation over different energy functionals. The number of regions present in an image does not need to be known a priori, nor their photometric properties. The algorithm jointly estimates the number of regions, their photometries, and their contours. The required regularization is provided by defining a region as a connected set of pixels. The evolving contours in the image are represented by computational particles that move as driven by an energy-minimization algorithm. We present an efficient discrete algorithm that allows minimizing a range of well-known energy functionals under the topological constraint of regions being connected components.

Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing. Our algorithm provides on-line per-frame estimates of the detection and tracking confidence at run time. We validate the accuracy of the estimates and apply the algorithm to tracking microtubules in mitotic yeast cells. This is based on a likelihood function that accounts for the dominant noise sources in the imaging equipment. The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality. ABSTRACT Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing. Our algorithm provides on-line per-frame estimates of the detection and tracking confidence at run time. We validate the accuracy of the estimates and apply the algorithm to tracking microtubules in mitotic yeast cells. This is based on a likelihood function that accounts for the dominant noise sources in the imaging equipment. The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality.