Background. Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation\r\nof skeletogenesis. In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal\r\nabnormalities can also have implications for human medicine. True in vivo studies require noninvasive imaging techniques such as\r\nhigh-resolution microCT. However, the manual analysis of acquired images is both time consuming and subjective. Methods. We\r\nhave developed a system for automated image segmentation that entails object-based image analysis followed by the classification\r\nof the extracted image objects. For image segmentation, a rule set was developed using Definiens image analysis software. The\r\nclassification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and\r\nobserver bias while maintaining high accuracy. Applying the system to the quantification of long bone growth has allowed us\r\nto present the first true in ovo data for bone length growth recorded in the same chick embryos. Conclusions. The procedures\r\ndeveloped represent an innovative approach for the automated segmentation, classification, quantification, and visualization of\r\nmicroCT images. MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the\r\nmorpho- and embryogenesis of live chick embryos.
Loading....