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DAP_Atlas

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medical-imaging
whole-body-CT
anatomy-segmentation
autopet
multi-organ
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Upload DAP Atlas data (batch 1/6)

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - image-segmentation
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+ modality:
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+ - CT
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+ language: []
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+ tags:
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+ - medical-imaging
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+ - whole-body-CT
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+ - anatomy-segmentation
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+ - autopet
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+ - multi-organ
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+ pretty_name: DAP Atlas
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # DAP Atlas — Dense Anatomical Prediction Atlas
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+
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+ Whole-body CT dataset with **142 anatomical structures** segmented across 533 volumes.
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+ The CT images come from the AutoPET FDG-PET-CT-Lesions cohort; the segmentation masks
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+ were produced by Jaus et al. (2023) via knowledge aggregation across 14 source datasets,
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+ nnU-Net pseudo-labelling, and post-processing using anatomical guidelines.
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+
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+ ## Dataset Summary
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+
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+ | Field | Details |
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+ |---|---|
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+ | Modality | CT (whole-body) |
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+ | Body Part | Full body |
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+ | Subjects | 482 unique patients (533 CT volumes — some patients have multiple studies) |
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+ | Labels | 142 anatomical structures + background + unknown_tissue (144 entries, gap at ID 11) |
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+ | Volume Shape | typically 512×512×~390 (varies per study) |
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+ | Spacing | ~0.8 × 0.8 × 2.5 mm |
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+ | Total Size | ~55 GB |
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+ | Mask License | Apache-2.0 |
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+ | CT License | TCIA Restricted (free-use; TCIA fully public since 2025-07-07) |
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+
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+ ## Data Structure
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+
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+ ```
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+ DAP_Atlas/
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+ ├── images/
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+ │ └── AutoPET_<subjectID>_<studyUID5>.nii.gz # 533 CT volumes
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+ ├── masks/
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+ │ └── AutoPET_<subjectID>_<studyUID5>.nii.gz # 533 mask volumes (paired by filename)
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+ └── labels.json # ID → anatomical structure name
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+ ```
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+
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+ CT and mask filenames are identical — pair each `images/X.nii.gz` with `masks/X.nii.gz`.
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+ Mask voxels are uint8 with values in {0, 1, ..., 144} (with a gap at 11). See `labels.json`
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+ for the canonical ID → name mapping (sourced from Table 2 of the paper).
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+
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+ ## Splits
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+
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+ The released dataset has no official train/val/test split. All 533 cases form a single
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+ pool. Downstream papers carve their own subsets.
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+
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+ ## Notes
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+
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+ - **Mask source:** the published 533 NIfTI files are the V1 expert-validated masks
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+ (`Atlas_final_dataset_V1_533/`). The repo also distributes nnU-Net model weights
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+ (Task901 / Task902) for inference on new CTs; those are not redistributed here.
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+ - **Costa numbering** is reversed vs. TotalSegmentator (DAP `costa_1` = lowest rib).
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+ See https://github.com/alexanderjaus/AtlasDataset/issues/7 for the mapping.
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+ - **Sex-conditional labels** (prostate, uterus, etc.) appear only for matching sex.
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+ - ID 11 is intentionally absent.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{jaus2023towards,
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+ title = {Towards Unifying Anatomy Segmentation: Automated Generation of a
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+ Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines},
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+ author = {Jaus, Alexander and Seibold, Constantin and Hermann, Kelsey and
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+ Walter, Alexandra and Giske, Kristina and Haubold, Johannes and
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+ Kleesiek, Jens and Stiefelhagen, Rainer},
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+ journal = {arXiv preprint arXiv:2307.13375},
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+ year = {2023}
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+ }
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+
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+ @article{gatidis2022whole,
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+ title = {A whole-body FDG-PET/CT dataset with manually annotated tumor lesions},
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+ author = {Gatidis, Sergios and Hepp, Tobias and Früh, Marcel and others},
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+ journal = {Scientific Data},
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+ volume = {9},
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+ number = {1},
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+ pages = {601},
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+ year = {2022},
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+ doi = {10.1038/s41597-022-01718-3}
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+ }
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+ ```
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+
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+ ## Sources
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+
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+ - Original masks: https://github.com/alexanderjaus/AtlasDataset
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+ - AutoPET CTs (TCIA): https://www.cancerimagingarchive.net/collection/fdg-pet-ct-lesions/
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+ - Paper: https://arxiv.org/abs/2307.13375
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