Cell tracking using phase-adaptive shape prior.

Automated tracking of cell population is very crucial for quantitative measurements of dynamic cell-cycle behaviour of individual cells. This problem involves several subproblems and a high accuracy of each step is essential to avoid error propagation. In this paper, we propose a holistic three-component system to tackle this problem. For each phase, we first learn a mean shape as well as a model of the temporal dynamics of transformation, which are used for estimating a shape prior for the cell in the current frame. We then segment the cell using a level set-based shape prior model. Finally, we identify its phase based on the goodness-of-fit of the data to the segmentation model. This phase information is also used for fine-tuning the segmentation result. We evaluate the performance of our method empirically in various aspects and in tracking individual cells from HeLa H2B-GFP cell population. Highly accurate validation results confirm the robustness of our method in many realistic scenarios and the essentiality of each component of our integrating system.

Automatic measurement of volume percentage stroma in endometrial images using texture segmentation.

The popularity of digital microscopy and tissue microarrays allow the use of high-throughput imaging for pathology research. To coordinate with this new technique, it is essential to automate the process of extracting information from such high amount of images. In this paper, we present a new model called the Subspace Mumford-Shah model for texture segmentation of microscopic endometrial images. The model incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms a widely used method in bioimaging community called k-means segmentation since it can separate textures which are less separated in the full feature space, which confirm the usefulness of subspace clustering in texture segmentation. Experimental results also show that the proposed method is well performed on diagnosing premalignant endometrial disease and is very practical for segmenting image set sharing similar properties.