Lunch 12:30pm; Talk 1pm
Connectomics is the study of the dense structure of neurons in the brain and their connections. Neurobiologists can gain new insights into the relation between the brain’s structure and its function by studying the brain connectivity at the single cell level. Recent advances in Electron Microscopy enable high-throughput imaging of neural tissue at a resolution high enough to identify single synapses. At this resolution, a cubic millimeter of brain tissue leads to an image volume of about 1 Petabyte of data. These large amounts of data require novel computer vision based algorithms and scalable software frameworks to process this data. In this talk I will describe the development of RhoANA, our dense Automatic Neural Annotation framework, which we have developed in order to automatically align, segment and reconstruct a cubic millimeter of brain tissue.
Verena Kaynig-Fittkau is a lecturer at Harvard School of Engineering and Applied Sciences (SEAS). Her main interests are machine learning and computer vision applied to bio-medical images. Previously she was a postdoctoral fellow at SEAS in the Graphics, Vision and Interaction Group of Hanspeter Pfister, working on connectomics in close collaboration with Jeff Lichtman. She received her PhD in computer science in 2011 from ETH Zurich, working in the Pattern Analysis and Machine Learning Group headed by Joachin Buhmann. She also developed image processing approaches for electron microscopy images for Electron Microscopy ETH Zurich (EMEZ). She received her BSc in computer science in 2006 from the University of Hamburg.