Automated 3D Reconstruction of Neurons from Serial Electron Micrographs
David Markowitz, Princeton University
A typical neuron in the human brain receives thousands of synaptic inputs from neighboring cells and may direct its output signals to just as many targets. An important long-term goal in the field of neuroscience is to understand how this complex local connectivity shapes network dynamics and supports information processing in the brain. Toward this end, recent technical advances in serial section electron microscopy have provided us with a very high resolution 3D window onto neuronal ultrastructure and synaptic connectivity throughout large volumes of fixed tissue. Image stacks produced by high-throughput techniques such as Serial Block-Faced Scanning EM (SBF-SEM) are so large, however, that manual tracing of all anatomical features in a typical data set would take years. For this reason, automated 3D reconstruction is imperative. Currently, I am developing robust algorithms for edge detection and segmentation of electron micrographs, as well as algorithms for registration and merging of traced objects across sections in SBF-SEM data. In the near term, my goal is to demonstrate accurate and exhaustive 3D reconstruction of anatomical features within small sample volumes. Future work will apply this technique in a high-performance computing context by simultaneously reconstructing and then reassembling many sub-volumes from a large data set. Through this work, I hope to demonstrate a robust methodology for automated 3D reconstruction of neurons from serial EM data sets, which may be used to gain important new insights into the structure and function of neural systems.
Abstract Author(s): David A. Markowitz, David W. Tank<br />Departments of Molecular Biology and Physics<br />Princeton University