Segment and track neurons in 3D by repulsive snake method

We present a snake (active contour) model based on repulsive force to segment neurons obtained from microscopy. Based on these segmentation results, we track the neurons in 3D image to look for its branch structure. These segmentation results allow user to study morphology of neurons to further investigate neuronal function and connectivity. This repulsive snake model can successfully segment two or multiple neurons that are close to each other by some alternating repulsive force generated from the neighboring objects. We apply our results on real data to demonstrate the performance of our method. © 2005 IEEE.published_or_final_versio

A novel method to analyze leukocyte rolling behavior in vivo

Leukocyte endothelial cell interaction is a fundamentally important process in many disease states. Current methods to analyze such interactions include the parallel-plate flow chamber and intravital microscopy. Here, we present an improvement of the traditional intravital microscopy that allows leukocyte-endothelial cell interaction to be studied from the time the leukocyte makes its initial contact with the endothelium until it adheres to or detaches from the endothelium. The leukocyte is tracked throughout the venular tree with the aid of a motorized stage and the rolling and adhesive behavior is measured off-line. Because this method can involve human error, methods to automate the tracking procedure have been developed. This novel tracking method allows for a more detailed examination of leukocyte-endothelial cell interactions

A novel framework for tracking in-vitro cells in time-lapse phase contrast data

With the proliferation of modern microscopy imaging technologies the amount of data that has to be analysed by biologists is constantly increasing and as a result the development of automatic approaches that are able to track cellular structures in timelapse images has become an important field of research. The aim of this paper is to detail the development of a novel tracking framework that is designed to extract the cell motility indicators in phase-contrast image sequences. To address issues that are caused by nonstructured (random) motion and cellular agglomeration, cell tracking is formulated as a sequential process where the inter-frame cell association is achieved by assessing the variation in the local structures contained in consecutive frames of the image sequence. We have evaluated the proposed algorithm on dense phase contrast cellular data and the reported results indicate that the developed algorithm is able to accurately track MadinDarby Canine Kidney (MDCK) Epithelial Cells in image data that is characterised by low contrast and high level of noise

Tracking cells in Life Cell Imaging videos using topological alignments

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