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license: mit
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# Brain Hemorrhage Segmentation Dataset (BHSD)
## Description
The Brain Hemorrhage Segmentation Dataset (BHSD) is a 3D multi-class segmentation dataset for intracranial hemorrhage (ICH). Intracranial hemorrhage is a pathological condition characterized by bleeding within the skull or brain, which can arise from various factors. Accurately identifying, localizing, and quantifying ICH is crucial for clinical diagnosis and treatment. Our dataset comprises 192 volumes with pixel-level annotations and unlabeled 1980 volumes across five ICH categories.
## Data Contents
This dataset includes the following two compressed files:
- **label_192.zip**: Contains 192 volumes with pixel-level annotations (Files need to be suffixed nii.gz).
- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/label_192.zip
- **unlabel_1980.zip**: Contains 1980 volumes of unannotated reconstructed data.
- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/unlabel_1980.zip
## Applications
This dataset is primarily intended to support the use of deep learning techniques in medical image segmentation tasks, particularly for multi-class segmentation of intracranial hemorrhages. It can be used for supervised and semi-supervised ICH segmentation tasks, and we provide experimental results with state-of-the-art models as reference benchmarks.
## Data Format
All volume data are stored in commonly used medical imaging formats (such as DICOM, NIFTI).
## Access and Usage
The dataset is now publicly available for download and is being used in the latest MICCAI competition. Researchers interested in using the dataset can download it and must comply with the relevant terms of use and copyright information.
## Copyright and License
This dataset is protected under copyright law. Please ensure compliance with the relevant license terms before use.
## Contact Information
For more information or to apply for access to the dataset, please contact us via:
- Email: [email protected]
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