Extended Data Fig. 2: Automated segmentation and synapse prediction.

a, Serial-section electron microscopy (EM) dataset from lobule V from the cyan boxed region in Fig. 1d. b, The 3D reconstruction segmentation pipeline. (i) EM image data, (ii) boundary affinities, and (iii) automated segmentation output. c, Parallelized volume processing using Daisy. The input dataset is divided into small blocks, on which multiple workers can dynamically query and work.Block completion status and output data are efficiently stored into a persistent database or on disk directly from the workers without going through the centralized scheduler process. d, Example view of targeted neuron reconstruction using merge-deferred segmentation (MD-Seg). Neurons are first segmented as small blocks, and inter-block merge decisions are deferred to proofreaders. This is illustrated by the different colored segments of the displayed neuron. The user interface is based on Neuroglancer, modified to provide the segment "grow" functionality, and to integrate an interface to the database keeping track of neuron name, cell type, completion status, notes, and which agglomeration threshold to use for "growing", as well as searching for neurons based on different criteria and recoloring segments of a single neuron to a single color ("Search DB" and "Color" tabs, not shown). e, Automated segmentation evaluation; plot points denote agglomeration thresholds. Average number of merge and split errors of (n = 9) 6 μm³ test volumes. We used a threshold (star) with 2.33 merges and 27 splits per 6 μm³ for proofreading. f, Automated synapse prediction evaluation; plot points denote connected component thresholds. Precision and recall curve for the synapse inference network. We achieved high synapse prediction accuracy with precision: 95.4% and recall: 92.2%, and an f-score: 93.8% (star).