  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
\url{https://github.com/songchangwei/3DCT-SD-IVH-ICH}.
