Recent　studies　have　supported　the　relation　between　mitochondrial　functions　and　degenerative　disorders　related　to　ageing,　such　as　Alzheimer＇s　and　Parkinson＇s　diseases.　Since　these　studies　have　exposed　the　need　for　detailed　and　high-resolution　analysis　of　physical　alterations　in　mitochondria,　it　is　necessary　to　be　able　to　perform　segmentation　and　3D　reconstruction　of　mitochondria.　However,　due　to　the　variety　of　mitochondrial　structures,　automated　mitochondria　segmentation　and　reconstruction　in　electron　microscopy　(EM)　images　have　proven　to　be　a　difficult　and　challenging　task.　This　paper　puts　forward　an　effective　and　automated　pipeline　based　on　deep　learning　to　realize　mitochondria　segmentation　in　different　EM　images.　The　proposed　pipeline　consists　of　three　parts:　(1)　utilizing　image　registration　and　histogram　equalization　as　image　pre-processing　steps　to　maintain　the　consistency　of　the　dataset;　(2)　proposing　an　effective　approach　for　3D　mitochondria　segmentation　based　on　a　volumetric,　residual　convolutional　and　deeply　supervised　network;　and　(3)　employing　a　3D　connection　method　to　obtain　the　relationship　of　mitochondria　and　displaying　the　3D　reconstruction　results.　To　our　knowledge,　we　are　the　first　researchers　to　utilize　a　3D　fully　residual　convolutional　network　with　a　deeply　supervised　strategy　to　improve　the　accuracy　of　mitochondria　segmentation.　The　experimental　results　on　anisotropic　and　isotropic　EM　volumes　demonstrate　the　effectiveness　of　our　method,　and　the　Jaccard　index　of　our　segmentation　(91.8%　in　anisotropy,　90.0%　in　isotropy)　and　F1　score　of　detection　(92.2%　in　anisotropy,　90.9%　in　isotropy)　suggest　that　our　approach　achieved　state-of-the-art　results.　Our　fully　automated　pipeline　contributes　to　the　development　of　neuroscience　by　providing　neurologists　with　a　rapid　approach　for　obtaining　rich　mitochondria　statistics　and　helping　them　elucidate　the　mechanism　and　function　of　mitochondria.