Using migrating cells as probes to illuminate features in live embryonic tissues.

The biophysical and biochemical properties of live tissues are important in the context of development and disease. Methods for evaluating these properties typically involve destroying the tissue or require specialized technology and complicated analyses. Here, we present a novel, noninvasive methodology for determining the spatial distribution of tissue features within embryos, making use of nondirectionally migrating cells and software we termed "Landscape," which performs automatized high-throughput three-dimensional image registration. Using the live migrating cells as bioprobes, we identified structures within the zebrafish embryo that affect the distribution of the cells and studied one such structure constituting a physical barrier, which, in turn, influences amoeboid cell polarity. Overall, this work provides a unique approach for detecting tissue properties without interfering with animal's development. In addition, Landscape allows for integrating data from multiple samples, providing detailed and reliable quantitative evaluation of variable biological phenotypes in different organisms.

Automatic classification of left ventricular wall segments in small animal ultrasound imaging.

Multiple statistics show that heart diseases are one of the main causes of mortality in our highly developed societies today. These diseases lead to a change of the physiology of the heart, which gives useful information about characteristic and severity of the defect. A fast and reliable diagnosis is the base for successful therapy. As a first step towards recognition of such heart remodeling processes, this work proposes a fully automatic processing pipeline for regional classification of the left ventricular wall in ultrasound images of small animals. The pipeline is based on state-of-the-art methods from computer vision and pattern classification. The myocardial wall is segmented and its motion is estimated. A feature extraction using the segmented data is realized to automatically classify the image regions into normal and abnormal myocardial tissue. The performance of the proposed pipeline is evaluated and a comparison of common classification algorithms on ultrasound data of living mice before and after artificially induced myocardial infarction is given. It is shown that the results of this work, reaching a maximum accuracy of 91.46%, are an encouraging base for further investigation.