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
Deguo Ma Chen Li Lin Qiao Tianming Du Dechao Tang Zhiyu Ma Marcin Grzegorzek Hongzan Hongzan Sun
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.