Angelou0516/totalsegmentator-organs

TotalSegmentator Organs Dataset

Dataset Description

The TotalSegmentator Organs dataset for multi-organ segmentation (TotalSegmentator Organs subset). This dataset contains CT scans with dense segmentation annotations.

Dataset Details

• Modality: CT
• Target: adrenal glands, colon, duodenum, esophagus, gallbladder, kidneys, liver, lungs, pancreas, small bowel, spleen, stomach, trachea, bladder
• Format: NIfTI (.nii.gz)

Dataset Structure

Each sample in the JSONL file contains:

{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ["organ1", "organ2", ...],
  "modality": "CT",
  "dataset": "TotalSegmentator_Organs",
  "official_split": "train",
  "patient_id": "patient_id"
}

Usage

Load Metadata

from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/totalsegmentator-organs")

# Access a sample
sample = ds['train'][0]
print(f"Patient ID: {sample['patient_id']}")
print(f"Image: {sample['image']}")
print(f"Mask: {sample['mask']}")
print(f"Labels: {sample['label']}")

Load Images

from huggingface_hub import snapshot_download
import nibabel as nib
import os

# Download the full dataset
local_path = snapshot_download(
    repo_id="Angelou0516/totalsegmentator-organs",
    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

@article{totalsegmentator_organs,
  title={TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images},
  year={2023}
}

License

CC-BY-4.0

Dataset Homepage

https://github.com/wasserth/TotalSegmentator