Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging
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Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging Ranga Srinivasan & Xiaobo Zhou & Eric Miller & Ju Lu & Jeff Litchman & Stephen T. C. Wong
Published online: 19 September 2007 # Humana Press Inc. 2007
Abstract This paper presents a new algorithm for extracting the centerlines of the axons from a 3D data stack collected by a confocal laser scanning microscope. Recovery of neuronal structures from such datasets is critical for quantitatively addressing a range of neurobiological questions such as the manner in which the branching pattern of motor neurons change during synapse elimination. Unfortunately, the data acquired using fluorescence microscopy contains many imaging artifacts, such as blurry boundaries and non-uniform intensities of fluorescent radiation. This makes the centerline extraction difficult. We propose a robust segmentation method based on probabilistic region merging to extract the centerlines of individual axons with minimal user interaction. The 3D model of the extracted axon centerlines in three datasets is R. Srinivasan : X. Zhou : S. T. C. Wong Harvard Center for Neurodegeneration and Repair–Center for Bioinformatics, Harvard Medical School, Boston, MA, USA R. Srinivasan Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA E. Miller Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA X. Zhou : S. T. C. Wong (*) Functional and Molecular Imaging Center, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA e-mail: [email protected] J. Lu : J. Litchman Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
presented in this paper. The results are validated with the manual tracking results while the robustness of the algorithm is compared with the published repulsive snake algorithm. Keywords Maximum intensity projection . Segmentation . Guided region growing . Watershed
Introduction The orientation of motor axons is critical in answering questions regarding synapse elimination in a developing muscle (Keller-Peck et al. 2001). Biologists have tried to address this issue by identifying the post-synaptic targets using transgenic mice that express fluorescent proteins in small subsets of motor axons. The post-synaptic targets are the cells innervated by the axons. More specifically, in the neuromuscular system, these are the muscle fibers. At neuromuscular junctions of developing mammals, the developing axonal branches of several motor neurons compete with each other resulting in withdrawal of all branches but one (Kasthuri and Lichtman 2003). The biological application of the developed algorithm is to reconstruct the entire innervation field within a skeletal muscle based on images acquired from confocal microscopy. Given a 3D image stack with non-uniform resolution in the x-, y- and z-direction, it is desirable to segment multiple axons contained in the neuron image and reduce them t
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