Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

翻译：脑出血是全球病死率最高、预后最差的疾病之一。自发性脑出血（SICH）通常呈急性发作，及时快速的影像学检查对出血的诊断、定位和量化至关重要。血肿周围水肿（PHE）的早期检测与精准分割对指导临床干预和改善患者预后具有关键作用。然而，由于可公开获取的脑CT影像数据集匮乏，针对PHE分割与检测的计算机辅助诊断方法的进展与评估面临挑战。本研究构建了一个名为PHE-SICH-CT-IDS的公开CT数据集，用于自发性脑出血中的血肿周围水肿研究。该数据集包含120例脑CT扫描共7022张CT图像，以及对应的患者医疗信息。为验证其有效性，我们评估了语义分割、目标检测和影像组学特征提取的经典算法。实验结果证实，PHE-SICH-CT-IDS适用于评估分割、检测及影像组学特征提取方法的性能。据我们所知，这是首个针对SICH中PHE的公开数据集，包含适用于多种临床场景的多样化数据格式。我们相信，PHE-SICH-CT-IDS将吸引研究者探索新算法，为临床医生和患者提供重要支持。PHE-SICH-CT-IDS已免费公开（仅限非商业用途），访问地址：https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937。