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README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- image-segmentation
+tags:
+- medical
+- MRI
+- prostate
+- segmentation
+- Medical Segmentation Decathlon
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+---
+
+# Medical Segmentation Decathlon: Prostate
+
+## Dataset Description
+
+This is the **Prostate** dataset from the Medical Segmentation Decathlon (MSD) challenge. The dataset contains MRI scans with segmentation annotations for prostate segmentation.
+
+### Dataset Details
+
+- **Modality**: MRI
+- **Task**: Task05_Prostate
+- **Target**: peripheral zone and transition zone
+- **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": ["label1", "label2", ...],
+  "modality": "MRI",
+  "dataset": "MSD_Prostate",
+  "official_split": "train",
+  "patient_id": "patient_id"
+}
+```
+
+### Data Organization
+
+```
+Task05_Prostate/
+├── imagesTr/          # Training images
+│   └── *.nii.gz
+└── labelsTr/          # Training labels
+    └── *.nii.gz
+```
+
+## Usage
+
+### Load Metadata
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+ds = load_dataset("Angelou0516/msd-prostate")
+
+# Access a sample
+sample = ds['train'][0]
+print(f"Image: {sample['image']}")
+print(f"Mask: {sample['mask']}")
+print(f"Labels: {sample['label']}")
+print(f"Modality: {sample['modality']}")
+```
+
+### 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/msd-prostate",
+    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
+
+If you use this dataset, please cite the Medical Segmentation Decathlon paper:
+
+```bibtex
+@article{antonelli2022medical,
+  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
+  author={Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and others},
+  journal={Nature Communications},
+  volume={13},
+  number={1},
+  pages={1--13},
+  year={2022},
+  publisher={Nature Publishing Group}
+}
+```
+
+## License
+
+CC-BY-SA-4.0
+
+## Dataset Homepage
+
+http://medicaldecathlon.com/