Fig. 3: NEURD graph decomposition enables automated proofreading.

a, Implementing domain knowledge as subgraph rules to automatically remove merge errors (see Supplementary Fig. 3 for more rules). b, Laminar distribution of merge errors (H01). The inhomogeneity of errors across different layers, possibly due to differences in neuropil density. The pial surface is to the right and slightly up (see Supplementary Fig. 7 for more details). c, Increased frequency of axon edits is observed in layer 5 of cortex (MICrONS). Pial surface is up. d, Dendritic errors in the MICrONS dataset increase near the top layers of the volume, where fine excitatory apical tufts lead to more frequent merges (see Supplementary Fig. 6 for more details). e,f, MICrONS (e) and H01 (f) synapse validation quantified by synapse precision and recall compared with manual proofreading (ground truth). ‘Before’ describes the accuracy of the raw segmentation prior to any proofreading. The substantial increases in precision ‘After’ automated proofreading (especially for axons) indicates that the cleaned neurons have good fidelity. The reduction in ‘After’ recall indicates the loss of some valid synapses in the automatic proofreading process (mostly concerning axons), while still retaining the majority of correct synapses (see also Supplementary Fig. 9 restricted to single somas). Dend, dendrite. g, An excitatory neuron from the MICrONS dataset in the 75th percentile of merge error skeletal length; identified merge errors are shown in red. h–k, Number of true-positive (TP) and false-positive (FP) axonal synapses from individual excitatory (h,i) or inhibitory (j,k) neurons in the validation set before (h,j) and after (i,k) automated proofreading, illustrating the large number of false-positive (red) synapses in the raw segmentation that are removed by automated proofreading (see Supplementary Figs. 8 and  9 for more details on the MICrONS dataset and Supplementary Fig. 10 for similar validation on the H01 dataset).