High content imaging-based assay to classify estrogen receptor-α ligands based on defined mechanistic outcomes.

Estrogen receptor-α (ER) is an important target both for therapeutic compounds and endocrine disrupting chemicals (EDCs); however, the mechanisms involved in chemical modulation of regulating ER transcriptional activity are inadequately understood. Here, we report the development of a high content analysis-based assay to describe ER activity that uniquely exploits a microscopically visible multi-copy integration of an ER-regulated promoter. Through automated single-cell analyses, we simultaneously quantified promoter occupancy, recruitment of transcriptional cofactors and large-scale chromatin changes in response to a panel of ER ligands and EDCs. Image-derived multi-parametric data was used to classify a panel of ligand responses at high resolution. We propose this system as a novel technology providing new mechanistic insights into EDC activities in a manner useful for both basic mechanistic studies and drug testing.

Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates.

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.