Add dataset README

README.md

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+---
+license: cc-by-nc-sa-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical-imaging
+  - ultrasound
+  - fetal-imaging
+  - segmentation
+  - miccai-2024
+pretty_name: ACOUSLIC-AI
+size_categories:
+  - n<1K
+---
+
+# ACOUSLIC-AI Dataset
+
+## Dataset Description
+
+ACOUSLIC-AI (Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries) is a dataset for the MICCAI 2024 challenge focused on fetal abdominal circumference measurement from ultrasound images.
+
+### Dataset Summary
+
+- **Source**: [Zenodo](https://zenodo.org/records/12697994)
+- **Challenge**: [ACOUSLIC-AI Grand Challenge](https://acouslic-ai.grand-challenge.org/)
+- **Version**: 1.1
+- **License**: CC-BY-NC-SA 4.0
+
+### Supported Tasks
+
+- Fetal abdomen segmentation
+- Abdominal circumference measurement
+- Ultrasound image analysis
+
+## Dataset Structure
+
+```
+.
+├── images/
+│   └── stacked_fetal_ultrasound/
+│       └── *.mha (300 files, ~335MB each)
+├── masks/
+│   └── stacked_fetal_abdomen/
+│       └── *.mha (300 files, ~335MB each)
+└── circumferences/
+    └── fetal_abdominal_circumferences_per_sweep.csv
+```
+
+### Data Fields
+
+**Images (.mha)**:
+- 3D volumetric ultrasound stacks
+- Format: MetaImage (.mha)
+- Each file: ~335MB
+
+**Masks (.mha)**:
+- Segmentation masks with pixel values:
+  - 0: Background
+  - 1: Optimal plane (fetal abdomen)
+  - 2: Suboptimal plane
+
+**Circumferences (CSV)**:
+- `uuid`: Unique identifier matching image/mask filenames
+- `subject_id`: Subject identifier
+- `sweep_1_ac_mm` to `sweep_6_ac_mm`: Abdominal circumference measurements in mm
+
+## Dataset Statistics
+
+| Type | Count | Format | Size |
+|------|-------|--------|------|
+| Images | 300 | .mha | ~335MB each |
+| Masks | 300 | .mha | ~335MB each |
+| CSV | 1 | .csv | 24KB |
+
+**Total size**: ~200GB
+
+## Usage
+
+```python
+from huggingface_hub import snapshot_download
+import SimpleITK as sitk
+
+# Download dataset
+local_dir = snapshot_download(
+    repo_id="Angelou0516/ACOUSLIC-AI",
+    repo_type="dataset"
+)
+
+# Load an image
+image_path = f"{local_dir}/images/stacked_fetal_ultrasound/0199616b-bdeb-4119-97a3-a5a3571bd641.mha"
+image = sitk.ReadImage(image_path)
+array = sitk.GetArrayFromImage(image)  # (D, H, W) numpy array
+
+# Load corresponding mask
+mask_path = f"{local_dir}/masks/stacked_fetal_abdomen/0199616b-bdeb-4119-97a3-a5a3571bd641.mha"
+mask = sitk.ReadImage(mask_path)
+mask_array = sitk.GetArrayFromImage(mask)
+```
+
+## Citation
+
+If you use this dataset, please cite the original source:
+
+```bibtex
+@misc{acouslic_ai_2024,
+  title={ACOUSLIC-AI: Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries},
+  year={2024},
+  publisher={Zenodo},
+  doi={10.5281/zenodo.12697994},
+  url={https://zenodo.org/records/12697994}
+}
+```
+
+## License
+
+This dataset is licensed under [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+## Acknowledgements
+
+This dataset was created for the MICCAI 2024 ACOUSLIC-AI Challenge.