A relational-tubular (ReTu) deformable model for vasculature quantification of zebrafish embryo from microangiography image series

Enhancement and Segmentation Workflow for the Developing Zebrafish Vasculature

Zebrafish have become an established in vivo vertebrate model to study cardiovascular development and disease. However, most published studies of the zebrafish vascular architecture rely on subjective visual assessment, rather than objective quantification. In this paper, we used state-of-the-art light sheet fluorescence microscopy to visualize the vasculature in transgenic fluorescent reporter zebrafish. Analysis of image quality, vascular enhancement methods, and segmentation approaches were performed in the framework of the open-source software Fiji to allow dissemination and reproducibility. Here, we build on a previously developed image processing pipeline; evaluate its applicability to a wider range of data; apply and evaluate an alternative vascular enhancement method; and, finally, suggest a work-flow for successful segmentation of the embryonic zebrafish vasculature.

An automated method for cell detection in zebrafish

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

Three-dimensional analysis of complex branching vessels in confocal microscopy images

The characteristic of confocal microscopy (CM) vascular data is that it contains many tiny vessels with branching and complex structure. In this work, an automated method for quantitative analysis and reconstruction of cerebral vessels from CM images is presented in which the extracted centerline of the vessels plays the key role. To assess the efficiency and accuracy of different centerline extraction methods, a comparison among three fully automated approaches is given. The centerline extraction methods studied in this work are a snake model, a path planning approach, and a distance transform-based method. To evaluate the accuracy of the quantitative parameters of vessels such as length and diameter, we apply the method to synthetic data. These results indicate that the snake model and the path planning method are more accurate in extracting the quantitative parameters. The efficiency of the approach in clinical applications is then confirmed by applying the method to real CM images. All three methods investigated in this work are accurate enough to correctly distinguish between normal and stroke brain data, while the snake model is the fastest for clinical applications. In addition, three-dimensional visualization, reconstruction, and characterization of CM vascular images of rat brains are presented.