Intracranial hemorrhage (ICH) is a pathological condition characterized by
bleeding inside the skull or brain, which can be attributed to various factors.
Identifying, localizing and quantifying ICH has important clinical
implications, in a bleed-dependent manner. While deep learning techniques are
widely used in medical image segmentation and have been applied to the ICH
segmentation task, existing public ICH datasets do not support the multi-class
segmentation problem. To address this, we develop the Brain Hemorrhage
Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset
containing 192 volumes with pixel-level annotations and 2200 volumes with
slice-level annotations across five categories of ICH. To demonstrate the
utility of the dataset, we formulate a series of supervised and semi-supervised
ICH segmentation tasks. We provide experimental results with state-of-the-art
models as reference benchmarks for further model developments and evaluations
on this dataset.

通过开发脑出血分割数据集（BHSD），本研究提供了一个包含 192 个像素级注释卷和 2200 个切片级注释卷的 3D 多类脑出血数据集，展示了该数据集在监督和半监督脑出血分割任务上的效用，并提供参考基准实验结果供进一步模型开发和评估使用。