BackgroundUnsupervised　traumatic　brain　injury　(TBI)　lesion　detection　aims　to　identify　and　segment　abnormal　regions,　such　as　cerebral　edema　and　hemorrhages,　using　only　healthy　training　data.　Recent　advancements　in　generative　models　have　achieved　success　in　unsupervised　anomaly　detection　by　transforming　abnormal　patterns　into　normal　counterparts.　However,　current　mask-free　image　generators　often　fail　to　maintain　semantic　consistency　of　anatomical　structures　during　the　restoration　process.　This　limitation　negatively　impacts　residual-based　anomaly　detection,　particularly　in　cases　where　structural　deformations　occur　due　to　the　mass　effect　of　TBI　lesions.PurposeThis　study　aims　to　develop　a　semantic-consistent,　unsupervised　TBI　lesion　detection　and　segmentation　method　that　minimizes　false　positives　by　preserving　normal　tissue　consistency　during　the　image　generation　process　while　addressing　mass　effect-related　tissue　deformations.MethodsWe　propose　the　semantic-consistent　diffusion　model　(SCDM)　for　unsupervised　TBI　lesion　detection,　focusing　on　the　localization　and　segmentation　of　various　lesion　types　from　noncontrast　CT　scans　of　TBI　patients.　Leveraging　the　high-quality　image　generation　capabilities　of　unconditioned　diffusion　models　(DM),　we　introduce　a　normal　tissue　retainment　(NTR)　regularization　to　ensure　that　normal　tissues　remain　unaltered　throughout　the　iterative　denoising　process.　Furthermore,　we　address　normal　tissue　compression　and　deformation　caused　by　the　mass　effect　of　TBI　lesions　through　diffeomorphic　registration,　reducing　erroneous　activations　in　residual　images　and　final　lesion　maps.ResultsExtensive　experiments　were　conducted　on　three　publicly　available　brain　lesion　datasets　and　one　internal　dataset.　These　datasets　comprised　75,　51,　92,　and　56　CT　scans,　respectively.　Thirty　seven　CT　scans　without　TBI　lesions　were　used　for　training　and　validation,　while　the　remaining　scans　were　used　for　testing.　The　proposed　method　achieved　average　DSC　of　0.56,　0.51,　0.47,　and　0.52　and　AUPRC　of　0.57,　0.48,　0.53,　and　0.50　on　the　BCIHM,　BHSD,　Seg-CQ500,　and　internal　datasets,　respectively,　surpassing　state-of-the-art　unsupervised　methods　for　TBI　lesion　detection　and　segmentation.　An　ablation　study　validated　the　effectiveness　of　the　proposed　NTR　regularization　and　diffeomorphic　registration-based　mass　effect　simulation.ConclusionsThe　results　suggest　that　the　proposed　SCDM　enables　effective　TBI　lesion　detection　and　segmentation　across　diverse　TBI　CT　scans.　It　significantly　reduces　false　positives　by　addressing　inconsistencies　in　normal　tissue　during　the　iterative　image　restoration　process　and　mitigating　mass　effect-induced　tissue　deformations.