NLM R01-LM009161

 

Title:

Neuronal Spines Tracking and Analysis for Time-Lapse, 3D Optical Microscopy

Description:

In this R01 project, we aim to develop automated analysis tools for 3D optical microscopy time-lapse of neuron images. It provides functions including spine feature extraction and spine tracking for data analysis and modeling as well as image registration, segmentation, and documentation. In addition, the module for data visualization offers a user friendly interface. These functions are packed in NeuronIQ software. NeuronIQ is a fully automated tool with several parameters being set before the processing. Thus, it is well suitable for batch processing a large dataset of images with little human interference. Manual editing function is provided in NeuronIQ. The user can load a specific image and check the result manually after the automated processing. The outliers can be removed one by one, or by dendrite pieces. The detection and measurement results are stored in Excel files. More specifically, the measurement of spine numbers, dendrite length, spine density, spine length, width of spine neck, area of spine section, length of spine perimeter, etc. are provided. 3D dendritic spine morphological classification using semi-supervised learning scheme helps to present accurate spine classification. We proposed a new dynamic tracking system based on the particle filter and the multiple hypotheses tracking algorithm (MHT). We also investigated other techniques such as dynamic programming to reduce the calculating time for practical purposes. We set up a concrete systems biological approach to model the pathways of dendritic spine morphogenesis and synapsogenesis at excitatory synapses as well. In summary, this project will provide an automated solution for neuron image analysis.

People:

PI: Stephen Wong, Ph.D., P.E., Department of Radiology, The Methodist Hospital Research Institute.
Co-PI: Bernando Sabatini, MD, Ph.D., Department of Neurobiology, Harvard Medical School.
http://neuro.med.harvard.edu/faculty/sabatini.html
Co-I: Zhong Xue, Ph.D., Department of Radiology, The Methodist Hospital Research Institute.
Co-I: Xiaofeng Xia, Ph.D., Department of Radiology, The Methodist Hospital Research Institute.

Progress:

The current release of NeuronIQ provides spine quantification information including spine numbers, dendrite length, spine density, spine length, width of spine neck, area of spine section, length of spine perimeter, etc. It also provides user manual correction. We are trying to package the spine classification with semi-supervised approach, spine tracking with particle filtering and 3D visualization model. Pathway studies of dendritic spine morphogenesis and synapsogenesis at excitatory synapses will be available in the near future. In addition, we also have collaborators in multiple institutes to validate the automatic image analysis capabilities of the current NeuronIQ software. The comparison between automated and manual analysis will be published soon.

Neuron image

Publications:

1. Zhu J, Chen X., Zhou X., and STC Wong, Application of NeuroIQ in Studying Perturbation of Dendritic Structure Caused by Genetic Mutations. A decade of neuroscience informatics: looking ahead -Workshop, Natcher conference center, NIH, April 26 - 27, 2004.

2. Zhou X, Zhu JM, Sabatini B, Wong STC, Mutual Information Based Feature Selection in Studying Perturbation of Dendritic Structure Caused by TSC2 Inactivation, Annual Meeting of Neuroscience, Washington DC, Nov., 2005.

3. Veronica A. Alvarez, Dennis A. Ridenour, and Bernardo L. Sabatini, Retraction of synapses and dendritic spines induced by off-target effects of RNA Interference. The Journal of Neuroscience, July, 26(30), 2006.

4. Xu X, Cheng J, Witt RM, Sabatini BL, and Wong STC, A shape analysis method to detect dendritic spine in 3D optical microscopy image, Biomedical Imaging: Macro to Nano, 3rd IEEE International Symposium on April 6, 2006 pp. 554 – 557.

5. Zhou X, Zhu J, Liu KY, Sabatini BL, and Wong STC, Mutual information-based feature selection in studying perturbation of dendritic dtructure caused by TSC2 inactivation. Neuroinformatics, 4(1), pp. 81-94. 2006.

6. J. Cheng, X. Zhou, E. Miller, R.M. Witt, J. Zhu, B.L.Sabatini and S. Wang, A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy. Journal of Neuroscience Method, Vol. 165, issue 1, Sept. 2007.

7. Zhang Y, Zhou, X., Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong STC, Dendritic Spine Detection Using Curvilinear Structure Detector and LDA Classifier. NeuroImage, 36:346- 360. 2007.

8. Cheng J, Zhou X, Wong STC, NeuronIQ: A Novel Computational Approach for Automatic Dendrite Spines Detection and Analysis. The third IEEE-NIH LIfe Science Systems and Applications (LISSA 2007) workshop, Bethesda, Maryland, Nov. 8-9, 2007.

9. Zhou W, Li H, Zhou X, and Wong STC, A new algorithm for 3D dendritic spine detection. 2007 International Symposium on Computational Models for Life Sciences (CMLS 2007), Gold Coast, Queensland, Australia, December 17-19, 2007.

10. Zhang Y, Zhou X, Witt R, Sabatini B, Adjeroh D, Wong STC, Automated spine detection using curvilinear structure detector and LDA classifier. Accepted in the IEEE International Symposium on Biomedical Imaging (ISBI ’07), April 12-15, 2007, Washington DC, USA

11. Beck, D., Zhou, X., Pham, T., Sabatini, B., Wong, S.T.C., An image driven systems biology approach for neurodegenerative disease studies in the TSC-mTOR pathway. Proc. IEEE/NIH Life Science Systems and Applications Workshop 2009 (LISSA 2009, April 9-10, Bethesda, Maryland, USA)

12. Li, Q., Zhou, X., Deng, ZG, Baron, M., Teylan, MA, Kim, Y., and Wong, STC, A novel surface-based geometric approach for 3D dendritic spine detection from multi-photon excitation microscopy images. Accepted in the IEEE International Symposium on Biomedical Imaging (ISBI’09), June 28-July 1, 2009, Boston, USA

13. Fan, J., Zhou, X., Dy, J., Zhang, Y., Wong STC, An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of in vivo Mouse Models. Neuroinformatics, DOI: 10.1007/s12021-009-9047-0

14. Shi, P., Zhou, X., Li, Q., Baron, M., Teylan, M.A., Kim, Y., and Wong STC., Online Three-Dimensional Dendritic Spines Morphological Classification Based on Semi-supervised Learning. Accepted in the IEEE International Symposium on Biomedical Imaging (ISBI’09), June 28-July 1, 2009, Boston, USA

15. Zhang Y, Chen K, Baron M, Teylan MA, Kim Y, Song Z, Greengard P, Wong ST., A Neurocomputational Method for Fully Automated 3D Dendritic Spine Detection and Segmentation of Medium-sized Spiny Neurons. NeuroImage, 50 (2010) 1472–1484

16. Peng Shi, Xiaobo Zhou, Marta Lipinski, Alexei Degretev, and Stephen TC Wong., AnIntegrative High Content Analysis Pipeline for Alzheimer’s Disease Drug Discovery. 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB’10), Boston, Jul 11-Jul 13, 2010.

Software Link:

http://www.cbi-tmhs.org/Neuroniq