| --- |
| 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. |
|
|
