Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

ilastik: interactive machine learning for (bio)image analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance

Path-Tracing on a Heterogeneous Multi-GPU Cluster

Path tracing has been an offline rendering technique ever since. With the enormous increase of speed seen with today's GPU hardware, interactive generation of images with global illumination effects becomes more and more feasible. In the near future we will probably see photorealistic computer graphic rendered in real time. While image footage was formerly created on render farms, the challenge today is distributing work across a network of machines equipped with GPUs. In this work an approach to interactive global illumination is developed by assigning bidirectional path tracing workload to graphic cards in a cluster. Finally the gained performance increase is evaluated

Path-Tracing on a Heterogeneous Multi-GPU Cluster

Path tracing has been an offline rendering technique ever since. With the enormous increase of speed seen with today's GPU hardware, interactive generation of images with global illumination effects becomes more and more feasible. In the near future we will probably see photorealistic computer graphic rendered in real time. While image footage was formerly created on render farms, the challenge today is distributing work across a network of machines equipped with GPUs. In this work an approach to interactive global illumination is developed by assigning bidirectional path tracing workload to graphic cards in a cluster. Finally the gained performance increase is evaluated

Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models

Automated cell tracking methods are still error-prone. On very large data sets, uncertainty measures are thus needed to guide the expert to the most ambiguous events so these can be corrected with minimal effort. We present two easy-to-use methods to sample multiple proposal solutions from a tracking-by-assignment graphical model and experimentally evaluate the benefits of the uncertainty measures derived. Ex-pert time for proof-reading is reduced greatly compared to random selection of predicted events. Index Terms — Cell tracking, uncertainty, machine learn-ing, probabilistic graphical model