Precise　vertebrae　segmentation　is　essential　for　the　image-related　analysis　of　spine　pathologies　such　as　vertebral　compression　fractures　and　other　abnormalities,　as　well　as　for　clinical　diagnostic　treatment　and　surgical　planning.　An　automatic　and　objective　system　for　vertebra　segmentation　is　required,　but　its　development　is　likely　to　run　into　difficulties　such　as　low　segmentation　accuracy　and　the　requirement　of　prior　knowledge　or　human　intervention.　Recently,　vertebral　segmentation　methods　have　focused　on　deep　learning-based　techniques.　To　mitigate　the　challenges　involved,　we　propose　deep　learning　primitives　and　stacked　Sparse　autoencoder-based　patch　classification　modeling　for　Vertebrae　segmentation　(SVseg)　from　Computed　Tomography　(CT)　images.　After　data　preprocessing,　we　extract　overlapping　patches　from　CT　images　as　input　to　train　the　model.　The　stacked　sparse　autoencoder　learns　high-level　features　from　unlabeled　image　patches　in　an　unsupervised　way.　Furthermore,　we　employ　supervised　learning　to　refine　the　feature　representation　to　improve　the　discriminability　of　learned　features.　These　high-level　features　are　fed　into　a　logistic　regression　classifier　to　fine-tune　the　model.　A　sigmoid　classifier　is　added　to　the　network　to　discriminate　the　vertebrae　patches　from　non-vertebrae　patches　by　selecting　the　class　with　the　highest　probabilities.　We　validated　our　proposed　SVseg　model　on　the　publicly　available　MICCAI　Computational　Spine　Imaging　(CSI)　dataset.　After　configuration　optimization,　our　proposed　SVseg　model　achieved　impressive　performance,　with　87.39%　in　Dice　Similarity　Coefficient　(DSC),　77.60%　in　Jaccard　Similarity　Coefficient　(JSC),　91.53%　in　precision　(PRE),　and　90.88%　in　sensitivity　(SEN).　The　experimental　results　demonstrated　the　method＇s　efficiency　and　significant　potential　for　diagnosing　and　treating　clinical　spinal　diseases.