Pakawat-Phasook/ClinFuseDiff-APIS-Data

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)