---
license: cc-by-4.0
task_categories:
- image-segmentation
- image-to-image
tags:
- medical
- neuroimaging
- stroke
- image-fusion
pretty_name: APIS Stroke Dataset (Lesion Cases Only)
size_categories:
- n<1K
---

# APIS Stroke Dataset - Preprocessed (Lesion Cases Only)

This dataset contains **54 acute ischemic stroke cases** with expert lesion annotations from the APIS dataset.

## Dataset Structure

```
preproc/
  train_000/
    ct.nii.gz              # CT scan
    mri.nii.gz             # Registered MRI (ADC)
    brain_mask.nii.gz      # Brain ROI mask (TotalSegmentator)
    bone_mask.nii.gz       # Bone/skull ROI mask (TotalSegmentator)
    lesion_mask.nii.gz     # Expert-annotated lesion segmentation
  train_001/
    ...
  (54 cases total)

splits/
  train.txt              # 37 cases (68.5%)
  val.txt                # 8 cases (14.8%)
  test.txt               # 9 cases (16.7%)
  split_metadata.json    # Split statistics
```

## Excluded Cases

6 cases without lesions were excluded:
- train_027, train_038, train_048, train_051, train_058, train_059

## Usage

```python
from pathlib import Path
import nibabel as nib

# Download dataset
from huggingface_hub import snapshot_download
data_dir = snapshot_download(repo_id="Pakawat-Phasook/ClinFuseDiff-APIS-Data", repo_type="dataset")

# Load a case
case_dir = Path(data_dir) / "preproc" / "train_000"
ct = nib.load(case_dir / "ct.nii.gz")
mri = nib.load(case_dir / "mri.nii.gz")
lesion_mask = nib.load(case_dir / "lesion_mask.nii.gz")
```

## Citation

```bibtex
@article{li2023apis,
  title={APIS: A paired CT-MRI dataset with lesion labels for acute ischemic stroke},
  author={Li, Zongwei and others},
  journal={Scientific Data},
  year={2023}
}
```

## Preprocessing

- **Registration**: MRI (ADC) registered to CT using ANTs SyN
- **ROI Masks**: Generated using TotalSegmentator v2
- **Normalization**: CT windowed to brain (C=40, W=400 HU)
- **Format**: NIfTI (.nii.gz), isotropic 1mm spacing

## License

CC-BY-4.0 (original APIS dataset license)