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README.md
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---
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license: cc-by-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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- CT
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- segmentation
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- TotalSegmentator_Muscles
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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: test
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path: test.jsonl
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- split: train
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path: train.jsonl
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---
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# TotalSegmentator Muscles Dataset
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## Dataset Description
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The TotalSegmentator Muscles dataset for muscle segmentation (TotalSegmentator Muscles subset). This dataset contains CT scans with dense segmentation annotations.
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### Dataset Details
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- **Modality**: CT
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- **Target**: various muscle groups
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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": ["organ1", "organ2", ...],
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"modality": "CT",
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"dataset": "TotalSegmentator_Muscles",
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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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## 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/totalsegmentator-muscles")
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# Access a sample
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sample = ds['train'][0]
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print(f"Patient ID: {sample['patient_id']}")
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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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```
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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/totalsegmentator-muscles",
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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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```bibtex
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@article{totalsegmentator_muscles,
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title={TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images},
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year={2023}
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}
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```
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## License
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CC-BY-4.0
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## Dataset Homepage
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https://github.com/wasserth/TotalSegmentator
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