Background:　The　locations　and　shapes　of　synapses　are　important　in　reconstructing　connectomes　and　analyzing　synaptic　plasticity.　However,　current　synapse　detection　and　segmentation　methods　are　still　not　adequate　for　accurately　acquiring　the　synaptic　connectivity,　and　they　cannot　effectively　alleviate　the　burden　of　synapse　validation.　Results:　We　propose　a　fully　automated　method　that　relies　on　deep　learning　to　realize　the　3D　reconstruction　of　synapses　in　electron　microscopy　(EM)　images.　The　proposed　method　consists　of　three　main　parts:　(1)　training　and　employing　the　faster　region　convolutional　neural　networks　(R-CNN)　algorithm　to　detect　synapses,　(2)　using　the　z-continuity　of　synapses　to　reduce　false　positives,　and　(3)　combining　the　Dijkstra　algorithm　with　the　GrabCut　algorithm　to　obtain　the　segmentation　of　synaptic　clefts.　Experimental　results　were　validated　by　manual　tracking,　and　the　effectiveness　of　our　proposed　method　was　demonstrated.　The　experimental　results　in　anisotropic　and　isotropic　EM　volumes　demonstrate　the　effectiveness　of　our　algorithm,　and　the　average　precision　of　our　detection　(92.8%　in　anisotropy,　93.5%　in　isotropy)　and　segmentation　(88.6%　in　anisotropy,　93.0%　in　isotropy)　suggests　that　our　method　achieves　state-of-the-art　results.　Conclusions:　Our　fully　automated　approach　contributes　to　the　development　of　neuroscience,　providing　neurologists　with　a　rapid　approach　for　obtaining　rich　synaptic　statistics.