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Transgenic mice with Tie2- green fluorescent protein (GFP) are used as a model to study the kinetic distribution of the Cy5-siRNA delivered by lipid nanoparticles (LNP) into the liver. After the mouse is injected with the LNP, it undergoes a procedure of intra-vital multi-photon microscopy imaging over a period of two hours, during which the process for the nanoparticle to diffuse into the hepatocytes from the vasculature system is monitored. Since the images are obtained in-vivo, the quantification of Cy5 kinetics suffers from the moving field of view (FOV). A method is proposed to register the sequence of images through template matching. Based on the semi-automatic segmentations of the vessels in the common FOV, the registered images are segmented into three regions of interest (ROI) in which the Cy5 signals are quantified. Computation of the percentage signal strength in the ROIs over time allows for the analysis of the diffusion of Cy5-siRNA into the hepatocytes, and helps demonstrate the effectiveness of the Cy5-siRNA delivery vehicle.

High-throughput micro-CT imaging has been used in our laboratory to evaluate fetal skeletal morphology in developmental toxicology studies. Currently, the volume-rendered skeletal images are visually inspected and observed abnormalities are reported for compounds in development. To improve the efficiency and reduce human error of the evaluation, we implemented a framework to automate the evaluation process. The framework starts by dividing the skull into regions of interest and then measuring various geometrical characteristics. Normal/abnormal classification on the bone segments is performed based on identifying statistical outliers. In pilot experiments using rabbit fetal skulls, the majority of the skeletal abnormalities can be detected successfully in this manner. However, there are shape-based abnormalities that are relatively subtle and thereby difficult to identify using the geometrical features. To address this problem, we introduced a model-based approach and applied this strategy on the squamosal bone. We will provide details on this active shape model (ASM) strategy for the identification of squamosal abnormalities and show that this method improved the sensitivity of detecting squamosal-related abnormalities from 0.48 to 0.92.