Accurate　vertebrae　segmentation　from　medical　images　plays　an　important　role　in　clinical　tasks　of　surgical　planning,　diagnosis,　kyphosis,　scoliosis,　degenerative　disc　disease,　spondylolisthesis,　and　post-operative　assessment.　Although　the　structures　of　bone　have　high　contrast　in　medical　images,　vertebrae　segmentation　is　a　challenging　task　due　to　its　complex　structure,　abnormal　spine　curves,　and　unclear　boundaries.　In　recent　years,　deep　learning　has　been　widely　applied　in　the　segmentation　of　vertebrae　images.　In　this　paper,　towards　a　robust　and　automatic　segmentation　system,　we　present　an　overlapping　patch-based　convNet　(OP-convNet)　model　for　automatic　vertebrae　CT　images　segmentation.　Due　to　the　greater　memory　and　processing　costs　associated　with　3D　convolutional　neural　networks,　as　well　as　the　risk　of　over-fitting,　we　employ　overlapping　patches　in　segmentation　tasks　using　2D　convNet.　In　the　proposed　vertebrae　segmentation　method,　OP-convNet　effectively　keeps　the　local　information　contained　in　CT　images.　We　divide　CT　image　slices　into　equal-sized　square　overlapping　patches　and　applied　the　RUS-function　on　these　patches　for　class　balancing　to　minimize　computational　requirements.　Then,　these　patches　are　input　into　the　model　along　with　their　corresponding　ground　truth　patches.　This　method　has　been　evaluated　on　publicly　available　CT　images　from　the　MICCAI　CSI　workshop　challenge.　The　results　indicate　that　OP-convNet　has　precision　(PRE)　of　90.1%,　specificity　(SPE)　of　99.4%,　accuracy　(ACC)　of　98.8%,　F-score　of　90.1%　in　terms　of　the　patch-based　classification　accuracy,　and　BF-score　of　90.2%,　sensitivity　(SEN)　of　90.3%,　Jaccard　index　(JAC)　of　82.3%,　dice　similarity　score　(DSC)　of　89.9%　in　terms　of　the　segmentation　accuracy　that　outperform　previous　methods　across　all　metrics