---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- cta
- ct
- aorta
- aortic-dissection
- vascular
- 3d
pretty_name: Aortic Dissection (Graz) - true/false lumen CTA
size_categories:
- n<1K
---

# Aortic Dissection (Graz) - CTA with true / false lumen expert annotations

Re-host of **"Aortic Dissection Dataset and Segmentations"**
([figshare 10.6084/m9.figshare.22269091](https://doi.org/10.6084/m9.figshare.22269091),
CC BY 4.0) - 40 **type-B aortic dissection** CTA volumes collected in clinical
routine 2005-2021 across the Medical University of Graz regional hospital
network, with expert **true-lumen** and **false-lumen** annotations.

> Mayer C, Pepe A, Hossain S, Karner B, Arnreiter M, Kleesiek J, Schmid J,
> Janisch M, Fuchsjaeger M, Deutschmann H, Zimpfer D, Egger J, Maechler H.
> *Type B Aortic Dissection CTA Collection with True and False Lumen Expert
> Annotations for the Development of AI-based Algorithms.*
> **Scientific Data 11, 596 (2024).** doi:10.1038/s41597-024-03284-2

Ethics: Medical University of Graz EK-34-161 ex 21/22; consent waived
(retrospective), head and face cropped for anonymisation.

## Contents

| | |
|---|---|
| Modality | CTA, arterial phase (**not** ECG-gated) |
| Cases | 40, one volume each |
| Classes | `0` background, `1` true lumen, `2` false lumen |
| Split | **none** - single cohort of 40 (`split="train"` for all rows) |
| Shape | mostly 512x512xZ; in-plane spacing 0.525-0.965 mm, through-plane 1.25-3.0 mm |
| Foreground | TL 125,516-659,055 voxels; FL 25,242-496,428 voxels; **no empty masks** |

**There is no thrombus class.** Per the paper, "clearly thrombosed sections have
not been segmented"; calcifications and atherosclerotic changes are likewise
excluded. Ambiguous dark regions judged to be late-filling artifact were
segmented *as lumen*. If you need a thrombus label, see ImageTBAD instead.

## Layout

```
dataset/ctaNNs/ctaNNs.nrrd           image, int16 HU - byte-identical to figshare
               truelumenNN.seg.nrrd  true lumen,  uint8 {0,1}
               falselumenNN.seg.nrrd false lumen, uint8 {0,1}
               maskNN.nrrd           merged {0=bg, 1=TL, 2=FL}
               meshNN.stl            surface mesh - byte-identical to figshare
original_masks/ctaNNs/*.seg.nrrd     untouched source masks (~13 MB total)
train.jsonl                          40 rows (schema below)
```

`train.jsonl` per row: `case_id`, `case_num`, `image`, `mask`,
`mask_true_lumen`, `mask_false_lumen`, `mesh`, `original_mask_*`, `shape`,
`spacing_mm`, `coarse_axis`, `tl_voxels`, `fl_voxels`, `tl_volume_ml`,
`fl_volume_ml`, `tl_fl_overlap_voxels`, `fl_label_value_raw`,
`resampled_to_image_grid`, `annotator_tier`, `split`.

## What was normalised here (and why)

Everything below is flagged per-case in `train.jsonl`, and the untouched source
masks ship under `original_masks/`.

1. **False-lumen masks binarised to `{0,1}`.** 17 of 40 cases store the false
   lumen with **label value 2**, the other 23 with 1 - cases
   `4, 5, 11, 12, 13, 14, 15, 16, 17, 18, 22, 25, 26, 30, 35, 36, 38`. Code
   doing `mask == 1` against the raw files silently drops those 17 false
   lumens. Original value kept in `fl_label_value_raw`.
2. **Case 40 masks resampled onto the image grid.** As shipped, case 40's masks
   sit on a grid rotated ~6.85 deg about Z (512x1103x187 @0.545 mm) while its
   image is axis-aligned (512x512x401 @0.602 mm) - index-wise pairing raises
   `IndexError`. Nearest-neighbour world-space resampling retains **100.01% /
   100.04%** of physical volume, and mean HU inside the resampled masks is
   **409 HU (TL) / 339 HU (FL)**, i.e. contrast-filled arterial lumen and in
   line with the other 39 cases. Flagged by `resampled_to_image_grid`.
   Every other case's masks already match their image exactly.
3. **Merged `maskNN.nrrd` added**, painted FL first then TL, so the **true
   lumen wins ties** - matching the authors' own converter
   [`apepe91/AD_NRRD_TO_STL`](https://github.com/apepe91/AD_NRRD_TO_STL) (cited
   in the paper's Code Availability), which subtracts a dilated true lumen from
   the false lumen. Ties are negligible either way: 16 of 40 cases overlap by
   1-106 voxels, at most **0.028%** of the TL-or-FL union - flap-boundary
   jitter, not a nested structure. Because ties are resolved, the stored map is
   strictly disjoint, so `mask == 1` / `mask == 2` are exact. The two source
   binaries remain available if you prefer to fan out.

Deliberately **not** changed: no reorientation, no resampling of the other 39
cases, no intensity windowing, original filenames kept.

## Gotchas

- **Key masks off the FILENAME, never the segment name.** Internal Slicer names
  are inconsistent: case 2's true lumen is called `artery`, case 19's *false*
  lumen is also called `artery`, and elsewhere you find `true lumen 5`,
  `true_lumen`, `truelumen`, `True Lumen neu`, `flase lumen`, `False_Lumen`.
- **Cases 19 and 20 are sagittally reformatted**: the coarse 1.5 mm axis is
  NRRD axis 0, not axis 2 (all other 38 cases have it at axis 2). A hard-coded
  z-slicer emits ~950 thin reformats instead of ~240 native slices. Use
  `coarse_axis` from `train.jsonl`.
- **No official split.** Group any split you make on `case_id`; one patient owns
  exactly one volume, so a plain case-level split is safe.
- **Anisotropic**, up to 3.0 mm through-plane.
- The images are the anonymised, head/face-cropped reconstructions. The
  separate `raw CTs.zip` on figshare is **not** mirrored here on purpose: its
  case 24 is a *different reconstruction* (776x776x101 oblique @0.625/0.625/2.4
  mm) that does **not** align with the masks.

## Annotation provenance - one tier, all of it gold

All 40 masks were seeded semi-automatically (local thresholding / Grow Cut,
fill-between-slices interpolation, or region growing - the paper's Table 2 gives
the per-case method), then given **2-3 hours of manual slice-by-slice
paint/erase**, because the paper reports the semi-automated results "were not
precise enough for AI applications". A radiologist then checked every case.

`annotator_tier` records who did the manual work: 15 cases (`1-10, 26, 27, 28,
29, 39`, the paper's Table-1 nabla marker) by a medical student, the other 25 by
cardiac-surgery residents. The paper marks these deliberately *to show there is
no difference between software users* - so **all 40 are gold and none should be
filtered**; the column is there for stratification only.

**Measured vs published volumes.** The `tl_volume_ml` / `fl_volume_ml` values
here are measured directly from the mask files. They reproduce the paper's
Table 2 to within +/-1% for 35 of 40 cases (most within 0.1%), which
independently confirms the image-mask pairing and the binarisation. Five cases
(`5, 6, 7, 32, 36`) disagree in the **true-lumen column only**; in three of them
the paper's TL cell exactly equals its FL cell, which looks like a transcription
slip. Each mask file contains exactly one correctly-named segment, so this is a
table artifact, not extra structures in the data. Trust the values here.

## Related datasets / leakage

**No patient overlap with [`MedOtter/SegA`](https://huggingface.co/datasets/MedOtter/SegA)**
(the AVT collection: KiTS 20 + RIDER 18 + Dongyang 18 - US and Chinese cohorts).
The two share *authors* (Pepe, Egger), not patients, and AVT's few incidental
dissection cases carry only a single binary aortic-vessel-tree mask, not
lumen labels. No overlap with AortaSeg24 (Univ. of Florida), ImageTBAD
(Guangdong), TotalSegmentator (Basel), or any TCIA collection. No
cross-reference ID to other datasets exists; `case_id` (`cta01`-`cta40`) joins
only to Tables 1 and 2 of the paper.

## License

**CC BY 4.0** - redistribution and commercial use permitted with attribution.
Cite the Scientific Data paper above and the figshare record.