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in this paper we describe the segmentation and registration of multiple serial section electron microscopy (em) volumes, using a novel algorithm based on the theory of random sets. we show that voxels resulting from the segmentation of random sets are an excellent choice for the representation of the connectivity graph in the volume. we demonstrate the segmentation of serial sections of a large volume of mouse brain tissue. the algorithm demonstrates that is capable of segmenting a 3.5 tb volume of mouse brain tissue in under 9 hours. this time is almost 10 times faster than the current state-of-the-art (atum-sem) and 5 times faster than the current fastest (sbem). by using random sets, the segmentation of the 3.5 tb volume of mouse brain tissue involves a time of only 17 hours, that is, approximately 10 times faster than the current state-of-the-art.
to investigate the feasibility of automatic 3d reconstruction of entire nervous systems, we applied this random set segmentation method to a large volume (3.5 tb) of serial sections of mouse brain tissue acquired with electron microscopy. we generated a connectivity graph from the segmented data and applied it to the reconstruction of the entire mouse brain. the resulting reconstruction allows us to view the entire mouse brain in 3d, with the relative positions of neuronal cell bodies and synapses automatically identified. this study, therefore, demonstrates that 3d reconstruction of entire neural circuits is possible using a combination of novel image segmentation methods and serial section electron microscopy data. 3d9ccd7d82
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