Advanced instrumentation and cellular imaging techniques using high-throughput 3D electron microscopy are driving a new revolution in the exploration of complex biological systems by providing near seamless views across multiple scales of resolution. These datasets provide the necessary breadth and depth to analyze multicellular, cellular, and subcelluar structure across large swathes of neural tissue. While these new imaging procedures are generating extremely large datasets of enormous value, the quantities are such that no single user or even laboratory team can possibly analyze the full content of their own imaging activities through traditional means. To address this challenge, we propose to further develop and refine a prototype hybrid system for high-throughput segmentation of large neuropil datasets that: 1) advances automatic algorithms for segmentation of cellular and sub-cellular structures using machine learning techniques;2) couples these techniques to a scalable and flexible process or tool suite allowing multiple users to simultaneously review, edit and curate the results of these automatic approaches;and, 3) builds a knowledge base of training data guiding and improving automated processing. This system will allow project scientists to select areas of interest, execute automatic segmentation algorithms, and distribute workload, curate data, and deposit final results into the Cell Centered Database (Martone et al. 2008) via accessible web-interfaces.
Emerging techniques in cellular and subcellular 3D imaging are generating datasets of enormous value to the study of disease processes and to the pursuit of greater insight into the structure and function of the nervous system. To unlock the potential of these data, new solutions are needed to improve the capability to segment and label the individual molecular, subcellular and cellular components within very large volumetric expanses. To address this challenge, we propose a hybrid system for high-throughput segmentation of large neuropil datasets that advances machine learning algorithms for automatic segmentation and couples these techniques to a scalable tool suite for multiple users to simultaneously review, edit and curate results.
| Churas, Christopher; Perez, Alex J; Hakozaki, Hiroyuki et al. (2017) Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections. Bioinformatics 33:3145-3147 |
| Seyedhosseini, Mojtaba; Tasdizen, Tolga (2016) Semantic Image Segmentation with Contextual Hierarchical Models. IEEE Trans Pattern Anal Mach Intell 38:951-64 |
| Ting Liu; Seyedhosseini, Mojtaba; Tasdizen, Tolga (2016) Image Segmentation Using Hierarchical Merge Tree. IEEE Trans Image Process 25:4596-4607 |
| Jones, Cory; Liu, Ting; Cohan, Nathaniel Wood et al. (2015) Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images. J Neurosci Methods 246:13-21 |
| Perez, Alex J; Seyedhosseini, Mojtaba; Deerinck, Thomas J et al. (2014) A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front Neuroanat 8:126 |
| Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba et al. (2014) A modular hierarchical approach to 3D electron microscopy image segmentation. J Neurosci Methods 226:88-102 |
| Jones, Cory; Liu, Ting; Ellisman, Mark et al. (2013) Semi-Automatic Neuron Segmentation in Electron Microscopy Images Via Sparse Labeling. Proc IEEE Int Symp Biomed Imaging 2013:1304-1307 |
| Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga (2013) SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES. Proc IEEE Int Symp Biomed Imaging 2013:860-863 |
| Jurrus, Elizabeth; Watanabe, Shigeki; Giuly, Richard J et al. (2013) Semi-automated neuron boundary detection and nonbranching process segmentation in electron microscopy images. Neuroinformatics 11:5-29 |
| Jones, Cory; Sayedhosseini, Mojtaba; Ellisman, Mark et al. (2013) Neuron Segmentation in Electron Microscopy Images Using Partial Differential Equations. Proc IEEE Int Symp Biomed Imaging 2013:1457-1460 |
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