Acute　intracerebral　hemorrhage　is　a　life-threatening　condition　that　demands　immediate　medical　intervention.　Intraparenchymal　hemorrhage　(IPH)　and　intraventricular　hemorrhage　(IVH)　are　critical　subtypes　of　this　condition.　Clinically,　when　such　hemorrhages　are　suspected,　immediate　CT　scanning　is　essential　to　assess　the　extent　of　the　bleeding　and　to　facilitate　the　formulation　of　a　targeted　treatment　plan.　While　current　research　in　deep　learning　has　largely　focused　on　qualitative　analyses,　such　as　identifying　subtypes　of　cerebral　hemorrhages,　there　remains　a　significant　gap　in　quantitative　analysis　crucial　for　enhancing　clinical　treatments.　Addressing　this　gap,　our　paper　introduces　a　dataset　comprising　222　CT　annotations,　sourced　from　the　RSNA　2019　Brain　CT　Hemorrhage　Challenge　and　meticulously　annotated　at　the　voxel　level　for　precise　IPH　and　IVH　segmentation.　This　dataset　was　utilized　to　train　and　evaluate　seven　advanced　medical　image　segmentation　algorithms,　with　the　goal　of　refining　the　accuracy　of　segmentation　for　these　hemorrhages.　Our　findings　demonstrate　that　this　dataset　not　only　furthers　the　development　of　sophisticated　segmentation　algorithms　but　also　substantially　aids　scientific　research　and　clinical　practice　by　improving　the　diagnosis　and　management　of　these　severe　hemorrhages.　Our　dataset　and　codes　are　available　at　https://github.com/songchangwei/3DCT-SD-IVH-ICH.　©　2024　IEEE.