metadata
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
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
@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)