Computed　tomography　(CT)　is　the　primary　diagnostic　tool　for　brain　diseases.　To　determine　the　appropriate　treatment　plan,　it　is　necessary　to　ascertain　the　patient＇s　bleeding　volume.　Automatic　segmentation　algorithms　for　hemorrhagic　lesions　can　significantly　improve　efficiency　and　avoid　treatment　delays.　However,　for　deep　supervised　learning　algorithms,　a　large　amount　of　labeled　training　data　is　usually　required,　making　them　difficult　to　apply　clinically.　In　this　study,　we　propose　an　unsupervised　domain　adaptation　method　that　is　an　unsupervised　domain　adaptation　segmentation　model　that　can　be　trained　across　modalities　and　diseases.　We　call　it　AMD-DAS　for　brain　CT　hemorrhage　segmentation　tasks.　This　circumvents　the　heavy　data　labeling　task　by　converting　the　source　domain　data　(MRI　with　glioma)　to　our　task＇s　required　data　(CT　with　Intraparenchymal　hemorrhage　(IPH)).　Our　model　implements　a　two-stage　domain　adaptation　process　to　achieve　this　objective.　In　the　first　stage,　we　train　a　pseudo-CT　image　synthesis　network　using　the　CycleGAN　architecture　through　a　matching　mechanism　and　domain　adaptation　approach.　In　the　second　stage,　we　use　the　model　trained　in　the　first　stage　to　synthesize　the　pseudo-CT　images.　We　use　the　pseudo-CT　with　source　domain　labels　and　real　CT　images　to　train　a　domain-adaptation　segmentation　model.　Our　method　exhibits　a　better　performance　than　the　basic　one-stage　domain　adaptation　segmentation　method　(+11.55　Dice　score)　and　achieves　an　86.93　Dice　score　in　the　IPH　unsupervised　segmentation　task.　Our　model　can　be　trained　without　using　a　ground-truth　label,　therefore　increasing　its　application　potential.　Our　implementation　is　publicly　available　at　https://github.com/GuanghuiFU/AMD-DAS-Brain-CT-Segmentation.