Familial　cerebral　cavernous　malformation　(FCCM)　is　a　hereditary　disorder　characterized　by　abnormal　vascular　structures　within　the　central　nervous　system.　The　FCCM　lesions　are　often　numerous　and　intricate,　making　quantitative　analysis　of　the　lesions　a　labor-intensive　task.　Consequently,　clinicians　face　challenges　in　quantitatively　assessing　the　severity　of　lesions　and　determining　whether　lesions　have　progressed.　To　alleviate　this　problem,　we　propose　a　quantitative　statistical　framework　for　FCCM,　which　comprises　an　efficient　annotation　module,　an　FCCM　lesion　segmentation　module,　and　an　FCCM　lesion　quantitative　statistics　module.　Our　framework　demonstrates　precise　segmentation　of　the　FCCM　lesion　based　on　efficient　data　annotation,　achieving　a　Dice　coefficient　of　91.09%.　More　importantly,　we　focus　on　3D　quantitative　statistics　of　lesions,　which　is　combined　with　image　registration　to　realize　the　quantitative　comparison　of　lesions　between　different　examinations　of　patients.　A　visualization　framework　has　also　been　established　for　doctors　to　comprehensively　compare　and　analyze　lesions.　The　experimental　results　have　demonstrated　that　our　proposed　framework　not　only　obtains　objective,　accurate,　and　comprehensive　quantitative　statistical　information,　which　provides　a　quantitative　assessment　method　for　disease　progression　and　drug　efficacy　study,　but　also　considerably　reduces　the　manual　measurement　and　statistical　workload　of　lesions.　This　highlights　the　potential　of　practical　application　of　the　framework　in　FCCM　clinical　research　and　clinical　decision-making.