Detecting and tracking motion of Myxococcus xanthus bacteria in swarms

A Hybrid Approach for Segmentation and Tracking of Myxococcus Xanthus Swarms

Cell segmentation and motion tracking in time-lapse images are fundamental problems in computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a type of rod-like cells with highly coordinated motion. The segmentation and tracking of M. xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult to identify individually in an accurate manner. The known cell tracking approaches mainly fall into two frameworks, detection association and model evolution, each having its own advantages and disadvantages. In this paper, we propose a new hybrid framework combining these two frameworks into one and leveraging their complementary advantages. Also, we propose an active contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid framework. Evaluated by 10 different datasets, our approach achieves considerable improvement over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and higher segmentation accuracy than performing segmentation in individual 2D images.

A matching model based on earth mover's distance for tracking Myxococcus xanthus

Tracking the motion of Myxococcus xanthus is a crucial step for fundamental bacteria studies. Large number of bacterial cells involved, limited image resolution, and various cell behaviors (e.g., division) make tracking a highly challenging problem. A common strategy is to segment the cells first and associate detected cells into moving trajectories. However, known detection association algorithms that run in polynomial time are either ineffective to deal with particular cell behaviors or sensitive to segmentation errors. In this paper, we propose a polynomial time hierarchical approach for associating segmented cells, using a new Earth Mover's Distance (EMD) based matching model. Our method is able to track cell motion when cells may divide, leave/enter the image window, and the segmentation results may incur false alarm, detection lost, and falsely merged/split detections. We demonstrate it on tracking M. xanthus. Applied to error-prone segmented cells, our algorithm exhibits higher track purity and produces more complete trajectories, comparing to several state-of-the-art detection association algorithms.

An integrated framework for automatic Ki-67 scoring in pancreatic neuroendocrine tumor

The Ki-67 labeling index is a valid and important biomarker to gauge neuroendocrine tumor cell progression. Automatic Ki-67 assessment is very challenging due to complex variations of cell characteristics. In this paper, we propose an integrated learning-based framework for accurate Ki-67 scoring in pancreatic neuroendocrine tumor. The main contributions of our method are: a novel and robust cell detection algorithm is designed to localize both tumor and non-tumor cells; a repulsive deformable model is applied to correct touching cell segmentation; a two stage learning-based scheme combining cellular features and regional structure information is proposed to differentiate tumor from non-tumor cells (such as lymphocytes); an integrated automatic framework is developed to accurately assess the Ki-67 labeling index. The proposed method has been extensively evaluated on 101 tissue microarray (TMA) whole discs, and the cell detection performance is comparable to manual annotations. The automatic Ki-67 score is very accurate compared with pathologists' estimation.