File size: 2,025 Bytes
ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c ea9d954 65d312c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
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)
|