“A system for scalable 3D visualization and editing of connectomic data” is a Master of Engineering thesis written under the guidance of H. Sebastian Seung of The Seung Lab at MIT’s Brain + Cognitive Sciences Department. In investigates the design and implementation of a system for working with real world neural tissue data that has been segmented for connectomic analysis.
The new field of connectomics is using technological advances in microscopy and neural computation to form a detailed understanding of structure and connectivity of neurons. Using the vast amounts of imagery generated by light and electron microscopes, connectomic analysis segments the image data to define 3D regions, forming neural-networks called connectomes….This thesis describes a scalable system for visualizing large connectomic data using multiple resolution meshes for performance while providing focused voxel rendering when editing for precision. After pre-processing a given set of data, users of the system are able to visualize neural data in real-time while having the ability to make detailed adjustments at the single voxel scale. The design and implementation of the system are discussed and evaluated.
The paper is available to download from DSpace@MIT.