Background　and　Objective:　The　goal　of　micro-connectomics　research　is　to　reconstruct　the　connectome　and　elucidate　the　mechanisms　and　functions　of　the　nervous　system　via　electron　microscopy　(EM).　Due　to　the　enormous　variety　of　neuronal　structures,　neuron　segmentation　is　among　most　difficult　tasks　in　connectome　reconstruction,　and　neuroanatomists　desperately　need　a　reliable　neuronal　structure　segmentation　method　to　reduce　the　burden　of　manual　labeling　and　validation.　Methods:　In　this　article,　we　proposed　an　effective　deep　learning　method　based　on　a　deep　residual　contextual　and　subpixel　convolution　network　to　obtain　the　neuronal　structure　segmentation　in　anisotropic　EM　image　stacks.　Furthermore,　lifted　multicut　is　used　for　post-processing　to　optimize　the　prediction　and　obtain　the　reconstruction　results.　Results:　On　the　ISBI　EM　segmentation　challenge,　the　proposed　method　ranks　among　the　top　of　the　leader　board　and　yields　a　Rand　score　of　0.98788.　On　the　public　data　set　of　mouse　piriform　cortex,　it　achieves　a　Rand　score　of　0.9562　and　0.9318　in　the　different　testing　stacks.　The　evaluation　scores　of　our　method　are　significantly　improved　when　compared　with　those　of　state-of-the-art　methods.　Conclusions:　The　proposed　automatic　method　contributes　to　the　development　of　micro-connectomics,　which　improves　the　accuracy　of　neuronal　structure　segmentation　and　provides　neuroanatomists　with　an　effective　approach　to　obtain　the　segmentation　and　reconstruction　of　neurons.　©　2022　The　Authors