Datasets:

Angelou0516
/

DAP_Atlas

license: apache-2.0
task_categories:
  - image-segmentation
modality:
  - CT
language: []
tags:
  - medical-imaging
  - whole-body-CT
  - anatomy-segmentation
  - autopet
  - multi-organ
pretty_name: DAP Atlas
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: sample_id
      dtype: string
    - name: subject_id
      dtype: string
    - name: study_uid_last5
      dtype: string
    - name: num_slices
      dtype: int32
    - name: ct_middle_slice
      dtype: image
    - name: mask_middle_slice
      dtype: image
    - name: overlay_middle_slice
      dtype: image
  splits:
    - name: train
      num_bytes: 178521349
      num_examples: 533
  download_size: 178530023
  dataset_size: 178521349

DAP Atlas — Dense Anatomical Prediction Atlas

Whole-body CT dataset with 142 anatomical structures segmented across 533 volumes. The CT images come from the AutoPET FDG-PET-CT-Lesions cohort; the segmentation masks were produced by Jaus et al. (2023) via knowledge aggregation across 14 source datasets, nnU-Net pseudo-labelling, and post-processing using anatomical guidelines.

Dataset Summary

Field	Details
Modality	CT (whole-body)
Body Part	Full body
Subjects	482 unique patients (533 CT volumes — some patients have multiple studies)
Labels	142 anatomical structures + background + unknown_tissue (144 entries, gap at ID 11)
Volume Shape	typically 512×512×~390 (varies per study)
Spacing	~0.8 × 0.8 × 2.5 mm
Total Size	~55 GB
Mask License	Apache-2.0
CT License	TCIA Restricted (free-use; TCIA fully public since 2025-07-07)

Data Structure

DAP_Atlas/
├── images/
│   └── AutoPET_<subjectID>_<studyUID5>.nii.gz   # 533 CT volumes
├── masks/
│   └── AutoPET_<subjectID>_<studyUID5>.nii.gz   # 533 mask volumes (paired by filename)
└── labels.json                                   # ID → anatomical structure name

CT and mask filenames are identical — pair each images/X.nii.gz with masks/X.nii.gz. Mask voxels are uint8 with values in {0, 1, ..., 144} (with a gap at 11). See labels.json for the canonical ID → name mapping (sourced from Table 2 of the paper).

Splits

The released dataset has no official train/val/test split. All 533 cases form a single pool. Downstream papers carve their own subsets.

Notes

Mask source: the published 533 NIfTI files are the V1 expert-validated masks (Atlas_final_dataset_V1_533/). The repo also distributes nnU-Net model weights (Task901 / Task902) for inference on new CTs; those are not redistributed here.
Costa numbering is reversed vs. TotalSegmentator (DAP costa_1 = lowest rib). See https://github.com/alexanderjaus/AtlasDataset/issues/7 for the mapping.
Sex-conditional labels (prostate, uterus, etc.) appear only for matching sex.
ID 11 is intentionally absent.

Citation

@article{jaus2023towards,
  title   = {Towards Unifying Anatomy Segmentation: Automated Generation of a
             Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines},
  author  = {Jaus, Alexander and Seibold, Constantin and Hermann, Kelsey and
             Walter, Alexandra and Giske, Kristina and Haubold, Johannes and
             Kleesiek, Jens and Stiefelhagen, Rainer},
  journal = {arXiv preprint arXiv:2307.13375},
  year    = {2023}
}

@article{gatidis2022whole,
  title   = {A whole-body FDG-PET/CT dataset with manually annotated tumor lesions},
  author  = {Gatidis, Sergios and Hepp, Tobias and Früh, Marcel and others},
  journal = {Scientific Data},
  volume  = {9},
  number  = {1},
  pages   = {601},
  year    = {2022},
  doi     = {10.1038/s41597-022-01718-3}
}

Sources

Original masks: https://github.com/alexanderjaus/AtlasDataset
AutoPET CTs (TCIA): https://www.cancerimagingarchive.net/collection/fdg-pet-ct-lesions/
Paper: https://arxiv.org/abs/2307.13375