The　spine　is　a　vital　part　of　the　body＇s　skeletal　structure,　connecting　the　entire　skeletal　framework　and　providing　stability　to　the　torso,　head,　and　neck.　When　the　spine　is　diseased,　the　spatial　morphology　of　the　spine　will　change　accordingly.　Therefore,　spinal　spatial　pattern　analysis　is　a　crucial　method　to　assess　the　health　status　of　the　spine.　In　the　spatial　morphology　description　of　the　spine,　the　line　connecting　each　vertebra＇s　center　is　generally　used　as　the　spatial　morphology　of　the　spine.　Hence,　extracting　the　center　of　the　spine＇s　vertebra　becomes　crucial.　Currently,　the　vertebral　centers　in　three-dimensional　(3D)　space　are　mainly　extracted　using　the　extraction　of　vertebral　centers　in　a　single　image　on　a　two-dimensional　(2D)　MRI　or　constraints　on　vertebral　shapes　based　on　prior　knowledge.　Therefore,　this　paper　proposes　a　more　stable　and　objective　vertebral　center　extraction　method　with　strong　subjective　factors　and　poor　extraction　accuracy　for　the　current　whole　spine　vertebral　center　extraction;　it　also　analyzes　and　demonstrates　its　accuracy　and　stability.　The　specific　method　follows:　firstly,　the　spine　MR　images　in　the　sagittal　plane　are　trained　with　the　neural　network　by　binary　classification;　after　that,　segmentation　is　carried　out　by　using　a　neural　network,　and　then　each　spine　MRI　obtained　by　segmentation　is　used　to　perform　the　pixel　level＇s　output　of　probability　maps　based　on　each　vertebral　body;　finally,　the　3D　coordinates　of　all　the　pixel　points　of　the　whole　spine　MRI　sequences　are　extracted　and　superimposed　to　form　a　body,　which　is　combined　with　the　probability　maps　outputted　before　to　obtain　the　center　of　the　entire　spine.　The　experimental　results　showed　that　the　average　measurement　error　of　this　method　was　1.48　mm　for　23　vertebrae,　and　the　measurement　results　remained　unchanged　after　repeating　the　measurement　three　times,　which　was　better　than　other　measurement　methods　in　terms　of　accuracy　and　stability.　©　2024　ACM.