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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-segmentation |
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- image-to-image |
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tags: |
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- medical |
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- neuroimaging |
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- stroke |
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- image-fusion |
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pretty_name: APIS Stroke Dataset (Lesion Cases Only) |
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size_categories: |
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- n<1K |
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--- |
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# APIS Stroke Dataset - Preprocessed (Lesion Cases Only) |
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This dataset contains **54 acute ischemic stroke cases** with expert lesion annotations from the APIS dataset. |
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## Dataset Structure |
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``` |
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preproc/ |
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train_000/ |
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ct.nii.gz # CT scan |
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mri.nii.gz # Registered MRI (ADC) |
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brain_mask.nii.gz # Brain ROI mask (TotalSegmentator) |
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bone_mask.nii.gz # Bone/skull ROI mask (TotalSegmentator) |
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lesion_mask.nii.gz # Expert-annotated lesion segmentation |
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train_001/ |
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... |
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(54 cases total) |
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splits/ |
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train.txt # 37 cases (68.5%) |
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val.txt # 8 cases (14.8%) |
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test.txt # 9 cases (16.7%) |
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split_metadata.json # Split statistics |
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``` |
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## Excluded Cases |
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6 cases without lesions were excluded: |
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- train_027, train_038, train_048, train_051, train_058, train_059 |
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## Usage |
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```python |
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from pathlib import Path |
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import nibabel as nib |
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# Download dataset |
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from huggingface_hub import snapshot_download |
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data_dir = snapshot_download(repo_id="Pakawat-Phasook/ClinFuseDiff-APIS-Data", repo_type="dataset") |
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# Load a case |
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case_dir = Path(data_dir) / "preproc" / "train_000" |
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ct = nib.load(case_dir / "ct.nii.gz") |
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mri = nib.load(case_dir / "mri.nii.gz") |
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lesion_mask = nib.load(case_dir / "lesion_mask.nii.gz") |
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``` |
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## Citation |
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```bibtex |
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@article{li2023apis, |
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title={APIS: A paired CT-MRI dataset with lesion labels for acute ischemic stroke}, |
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author={Li, Zongwei and others}, |
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journal={Scientific Data}, |
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year={2023} |
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} |
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``` |
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## Preprocessing |
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- **Registration**: MRI (ADC) registered to CT using ANTs SyN |
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- **ROI Masks**: Generated using TotalSegmentator v2 |
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- **Normalization**: CT windowed to brain (C=40, W=400 HU) |
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- **Format**: NIfTI (.nii.gz), isotropic 1mm spacing |
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## License |
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CC-BY-4.0 (original APIS dataset license) |
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