Electron　microscopy　has　become　the　most　important　technique　in　the　field　of　connectomics.　Several　methods　have　been　proposed　in　the　literature　to　tackle　the　problem　of　dense　reconstruction.　However,　sparse　reconstruction,　which　is　a　promising　technique,　has　not　been　extensively　studied.　As　a　result,　we　develop　an　AI　integrated　system　for　sparse　reconstruction　that　can　automatically　trace　neurons　with　only　the　initial　seeded　masks.　First,　as　an　important　part　of　the　system　for　interlayer　information　estimation,　convolutional　LSTMs　are　employed　to　estimate　the　spatial　contexts　between　adjacent　sections.　Then,　the　intra-slice　information　is　obtained　by　a　lightweight　U-Net.　Moreover,　we　employ　a　novel　recursive　training　method　that　can　significantly　improve　the　performance.　To　reduce　the　tracing　errors　caused　by　misalignments　in　large-scale　data,　we　integrate　a　shift　estimation　and　correction　module　that　effectively　improves　the　traced　neuron　length.　To　the　best　of　our　knowledge,　this　is　the　first　attempt　to　apply　a　recurrent　neural　network　to　the　task　of　neuron　tracing.　In　addition,　our　approach　performs　better　than　other　state-of-the-art　methods　on　two　highly　anisotropic　datasets.