Connectomics is the study of the dense structure of the neurons in the brain and their synapses, providing new insights into the relation between brain’s structure and its function. Recent advances in Electron Microscopy enable high-resolution imaging (4nm per pixel) of neural tissue at a rate of roughly 10 terapixels in a single day, allowing neuroscientists to capture large blocks of neural tissue in a reasonable amount of time. The large amounts of data require novel computer vision based algorithms and scalable software frameworks to process this data. We describe RhoANA, our dense Automatic Neural Annotation framework, which we have developed in order to automatically align, segment and reconstruct a 1mm3 brain tissue (~2 peta-pixels).
The rodent brain is organized with length scales spanning centimeters to nanometers --6 orders of magnitude . At the centimeter scale, the brain consist of lobes of cortex, the cerebellum, the brainstem and the spinal cord. The millimeter scale have neurons arranged in columns, layers, or otherwise clustered. Recent technological imaging advances allow the generation of neuronal datasets spanning the spatial range from nanometers to 100s of microns [2,3]. Collecting a 1 mm3 volume dataset of brain tissue at 4 nm x-y resolution using the fastest signal-beam SEM would require ~6 years. To move to the next length and volume scale of neuronal circuits requires several technological advances. The multibeam scanning electron microscope (mSEM) represents a transformative imaging technology that enables neuroscientists to tackle millimeter scale cortical circuit problems. In this work we describe a workflow from tissue harvest to imaging that will generate a 2 petabyte dataset (> 300,000,000 images) of rat visual cortex imaged at a 4nm x 4nm x-y (Nyquist sampling of membranes) and 30nm section thickness in less than 6 months.