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Intracerebral hemorrhage is one of the diseases with the highest mortalityand poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH)typically presents acutely, prompt and expedited radiological examination iscrucial for diagnosis, localization, and quantification of the hemorrhage.Early detection and accurate segmentation of perihematomal edema (PHE) play acritical role in guiding appropriate clinical intervention and enhancingpatient prognosis. However, the progress and assessment of computer-aideddiagnostic methods for PHE segmentation and detection face challenges due tothe scarcity of publicly accessible brain CT image datasets. This studyestablishes a publicly available CT dataset named PHE-SICH-CT-IDS forperihematomal edema in spontaneous intracerebral hemorrhage. The datasetcomprises 120 brain CT scans and 7,022 CT images, along with correspondingmedical information of the patients. To demonstrate its effectiveness,classical algorithms for semantic segmentation, object detection, and radiomicfeature extraction are evaluated. The experimental results confirm thesuitability of PHE-SICH-CT-IDS for assessing the performance of segmentation,detection and radiomic feature extraction methods. To the best of ourknowledge, this is the first publicly available dataset for PHE in SICH,comprising various data formats suitable for applications across diversemedical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers toexplore novel algorithms, providing valuable support for clinicians andpatients in the clinical setting. PHE-SICH-CT-IDS is freely published fornon-commercial purpose at:https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic  Segmentation, Object Detection and Radiomic Feature Extraction of  Perihematomal Edema in Spontaneous Intracerebral Hemorrhage