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.

翻译：颅内出血（ICH）是一种以颅骨或脑内部出血为特征的病理状态，可由多种因素引起。以出血类型依赖的方式识别、定位和量化ICH具有重要的临床意义。尽管深度学习技术已广泛应用于医学图像分割并应用于ICH分割任务，但现有公开ICH数据集不支持多类分割问题。为解决这一问题，我们开发了脑出血分割数据集（BHSD），该数据集提供包含192个体素级标注样本和2200个切片级标注样本的三维多类ICH数据集，涵盖五种ICH类型。为展示该数据集的实用性，我们构建了一系列监督和半监督ICH分割任务，并基于最先进模型提供实验基准结果，以支持该数据集的后续模型开发与评估。