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