Abstract
The variations in dendritic branch morphology and spine density provide insightful information about the brain function and possible treatment to neurodegenerative disease, for example investigating structural plasticity during the course of Alzheimer’s disease. Most automated image processing methods aiming at analyzing these problems are developed for in vitro data. However, in vivo neuron images provide real time information and direct observation of the dynamics of a disease process in a live animal model. This paper presents an automated approach for detecting spines and tracking spine evolution over time with in vivo image data in an animal model of Alzheimer’s disease. We propose an automated pipeline starting with curvilinear structure detection to determine the medial axis of the dendritic backbone and spines connected to the backbone. We, then, propose the adaptive local binary fitting (aLBF) energy level set model to accurately locate the boundary of dendritic structures using the central line of curvilinear structure as initialization. To track the growth or loss of spines, we present a maximum likelihood based technique to find the graph homomorphism between two image graph structures at different time points. We employ dynamic programming to search for the optimum solution. The pipeline enables us to extract dynamically changing information from real time in vivo data. We validate our proposed approach by comparing with manual results generated by neurologists. In addition, we discuss the performance of 3D based segmentation and conclude that our method is more accurate in identifying weak spines. Experiments show that our approach can quickly and accurately detect and quantify spines of in vivo neuron images and is able to identify spine elimination and formation.
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Acknowledgement
The authors would like to thank members of the Center for Biomedical Informatics, The Methodist Hospital Research Institute, especially Zheng Xia and former members of the HCNR Center for Bioinformatics, Harvard Medical School. We also thank Dr Tara Spires-Jones and Dr Bradley Hyman for providing sample in vivo image data. This research is partially funded by NIH R01 AG028928 and NIH R01 LM009161 and by HCNR, Harvard Medical School.
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Fan, J., Zhou, X., Dy, J.G. et al. An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of In Vivo Mouse Models. Neuroinform 7, 113–130 (2009). https://doi.org/10.1007/s12021-009-9047-0
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DOI: https://doi.org/10.1007/s12021-009-9047-0