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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+tags:
+- medical
+- CT
+- multi-organ
+- segmentation
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+---
+
+# Beyond the Cranial Vault Dataset
+
+## Dataset Description
+
+The Beyond the Cranial Vault dataset for multi-organ abdominal CT segmentation. This dataset contains CT scans with dense segmentation annotations.
+
+### Dataset Details
+
+- **Modality**: CT
+- **Target**: 13 abdominal organs
+- **Format**: NIfTI (.nii.gz)
+
+### Dataset Structure
+
+Each sample in the JSONL file contains:
+```json
+{
+  "image": "path/to/image.nii.gz",
+  "mask": "path/to/mask.nii.gz",
+  "label": ["organ1", "organ2", ...],
+  "modality": "CT",
+  "dataset": "BTCV",
+  "official_split": "train",
+  "patient_id": "patient_id"
+}
+```
+
+## Usage
+
+### Load Metadata
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+ds = load_dataset("Angelou0516/btcv")
+
+# Access a sample
+sample = ds['train'][0]
+print(f"Patient ID: {sample['patient_id']}")
+print(f"Labels: {sample['label']}")
+```
+
+### Load Images
+
+```python
+from huggingface_hub import snapshot_download
+import nibabel as nib
+import os
+
+# Download the full dataset
+local_path = snapshot_download(
+    repo_id="Angelou0516/btcv",
+    repo_type="dataset"
+)
+
+# Load a sample
+sample = ds['train'][0]
+image = nib.load(os.path.join(local_path, sample["image"]))
+mask = nib.load(os.path.join(local_path, sample['mask']))
+
+# Get numpy arrays
+image_data = image.get_fdata()
+mask_data = mask.get_fdata()
+
+print(f"Image shape: {image_data.shape}")
+print(f"Mask shape: {mask_data.shape}")
+```
+
+## Citation
+
+```bibtex
+@article{btcv,
+  title={Multi-Atlas Labeling Beyond the Cranial Vault},
+  year={2023}
+}
+```
+
+## License
+
+CC-BY-4.0
+
+## Dataset Homepage
+
+https://www.synapse.org/#!Synapse:syn3193805