Together,　mitochondria　and　the　endoplasmic　reticulum　(ER)　occupy　more　than　20%　of　a　cell＇s　volume,　and　morphological　abnormality　may　lead　to　cellular　function　disorders.　With　the　rapid　development　of　large-scale　electron　microscopy　(EM),　manual　contouring　and　three-dimensional　(3D)　reconstruction　of　these　organelles　has　previously　been　accomplished　in　biological　studies.　However,　manual　segmentation　of　mitochondria　and　ER　from　EM　images　is　time　consuming　and　thus　unable　to　meet　the　demands　of　large　data　analysis.　Here,　we　propose　an　automated　pipeline　for　mitochondrial　and　ER　reconstruction,　including　the　mitochondrial　and　ER　contact　sites　(MAMs).　We　propose　a　novel　recurrent　neural　network　to　detect　and　segment　mitochondria　and　a　fully　residual　convolutional　network　to　reconstruct　the　ER.　Based　on　the　sparse　distribution　of　synapses,　we　use　mitochondrial　context　information　to　rectify　the　local　misleading　results　and　obtain　3D　mitochondrial　reconstructions.　The　experimental　results　demonstrate　that　the　proposed　method　achieves　state-of-the-art　performance.